Speech Recognition Accuracy with Natural-Language Understanding based Meta-Speech Systems for Assistant Systems

ABSTRACT

In one embodiment, a method includes receiving, at a client system via a client-side assistant process, a first audio input from a first user. The method includes generating multiple transcriptions corresponding to the first audio input based on multiple client-side automatic speech recognition (ASR) engines. Each ASR engine is associated with a respective domain out of multiple domains. The method includes determining, for each transcription, a combination of one or more tasks and one or more entities to be associated with the transcription. The method includes selecting one or more combinations of tasks and entities from the multiple combinations to be associated with the first audio input based on a determined selection strategy. The method includes presenting, via the client-side assistant process, a response to the first audio input based on the selected combinations.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 16/741,642, filed 13 Jan. 2020, which claims thebenefit, under 35 U.S.C. § 119(e), of U.S. Provisional PatentApplication No. 62/923,342, filed 18 Oct. 2019, which are incorporatedherein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. As an example and not by way of limitation, the assistantsystem may support both audio (verbal) input and nonverbal input, suchas vision, location, gesture, motion, or hybrid/multi-modal input. Theassistant system may create and store a user profile comprising bothpersonal and contextual information associated with the user. Inparticular embodiments, the assistant system may analyze the user inputusing natural-language understanding. The analysis may be based on theuser profile of the user for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may usedialog-management techniques to manage and advance the conversation flowwith the user. In particular embodiments, the assistant system mayfurther assist the user to effectively and efficiently digest theobtained information by summarizing the information. The assistantsystem may also assist the user to be more engaging with an onlinesocial network by providing tools that help the user interact with theonline social network (e.g., creating posts, comments, messages). Theassistant system may additionally assist the user to manage differenttasks such as keeping track of events. In particular embodiments, theassistant system may proactively execute, without a user input, tasksthat are relevant to user interests and preferences based on the userprofile, at a time relevant for the user. In particular embodiments, theassistant system may check privacy settings to ensure that accessing auser's profile or other user information and executing different tasksare permitted subject to the user's privacy settings.

In particular embodiments, the assistant system may assist the user viaa hybrid architecture built upon both client-side processes andserver-side processes. The client-side processes and the server-sideprocesses may be two parallel workflows for processing a user input andproviding assistance to the user. In particular embodiments, theclient-side processes may be performed locally on a client systemassociated with a user. By contrast, the server-side processes may beperformed remotely on one or more computing systems. In particularembodiments, an arbitrator on the client system may coordinate receivinguser input (e.g., an audio signal), determine whether to use aclient-side process, a server-side process, or both, to respond to theuser input, and analyze the processing results from each process. Thearbitrator may instruct agents on the client-side or server-side toexecute tasks associated with the user input based on the aforementionedanalyses. The execution results may be further rendered as output to theclient system. By leveraging both client-side and server-side processes,the assistant system can effectively assist a user with optimal usage ofcomputing resources while at the same time protecting user privacy andenhancing security.

In particular embodiment, the assistant system may utilize multipleautomatic speech recognition (ASR) engines to analyze an audio inputthrough a meta-speech engine. In order for an ASR engine to operate withsufficient accuracy, the ASR engine may require a large volume oftraining data to establish the foundation for the speech models thatcorrespond to the ASR engines. As an example and not by way oflimitation, the large volume of training data may comprise 100,000 audioinputs and their respective transcriptions. However, when initiallytraining a speech model, there may not be a sufficient volume oftraining data to build out the speech models with adequate operability.That is, as an example and not by way of limitation, the speech modelmay not have enough training data to be able to accurately generatetranscriptions for at least 95% of audio inputs. This may be determinedif the user needs to repeat a request and/or if there is an error ingenerating a transcription. Therefore, the speech model may require alarger volume of training data in order to accurately generate thetranscriptions of a threshold number of audio inputs. On the other hand,there may be ASR engines that have been trained on a limited data setrelated to a limited set of tasks that operate with sufficient accuracy(e.g., 95% of audio inputs are accurately transcribed) for that limitedset of tasks. As an example and not by way of limitation, there may bean ASR engine for messaging/calling, an ASR engine for music-relatedfunctions, and an ASR engine for default system operations. Forinstance, the ASR engine for messaging/calling may accurately transcribea threshold number of audio inputs (e.g., 95%) related tomessaging/calling requests. As such, an assistant system may leveragethe individual ASR engines in order to improve upon the accuracy of ASRresults. To do so, the assistant system may receive an audio input andsend the audio input to multiple ASR engines. By sending the audio inputto multiple ASR engines, each ASR engine may generate a transcriptionbased on the respective speech model of the ASR engine. This improvesupon the accuracy of the ASR results by increasing the probability of anaccurate transcription of the audio input. As an example and not by wayof limitation, if a user requests to play music using the assistantsystem, the audio input is sent to all of the available ASR engines, oneof which is the ASR engine for music-related functions. The ASR enginefor music-related functions may accurately transcribe the audio input tobe the request to play music. By sending the audio input correspondingto the request to play music to an ASR engine for music-relatedfunctions, the assistant system may improve the accuracy of thetranscription of the audio input because the particular ASR engine mayhave a large volume of training data corresponding to audio inputsassociated with music-related functions. By using multiple ASR engines,the assistant system may have a robust foundation of speech models tohandle and transcribe different requests, such as music-related requestsor messaging/calling-related requests. The use of multiple ASR engines,each associated with their own respective speech model, may help toavoid the need to extensively train one speech model on a large dataset,such as training one speech model to transcribe a request to play musicand also to transcribe a request for weather information. Theseindividual ASR engines may already be trained for their respectivefunctions and may not need any further training to achieve sufficientaccuracy for operability for their respective functions. Therefore, theuse of individual ASR engines may reduce or eliminate the time needed totrain a speech model to achieve adequate operability (e.g., be able toaccurately transcribe a threshold number of audio inputs) inhandling/transcribing audio inputs that are associated with a wide rangeof functions. The assistant system may take the output of the ASRengines and send that into a natural language understanding (NLU) modulethat identifies one or more intents and one or more slots associatedwith each output of the respective ASR engine. The output of the NLUmodule may be sent to a meta-speech engine, which may select one or moreintents and one or more slots associated with the received audio inputfrom the possible choices based on a selection strategy. The selectionstrategy may be, for example, a simple selection strategy, a systemcombination with a machine learning model strategy, or a rankedstrategy. After determining the intents and slots, the meta-speechengine may send the output to be processed by one or more agents of theassistant system. In particular embodiments, the meta-speech engine maybe a component of the assistant system that processes an audio input togenerate a combination of intents and slots. In particular embodiments,the meta-speech engine may comprise a plurality of ASR engines and anNLU module. Although this disclosure describes utilizing multiple ASRengines to analyze an audio input in a particular manner, thisdisclosure contemplates utilizing multiple ASR engines to analyze anaudio input in any suitable manner.

In particular embodiments, the assistant system may receive, from aclient system associated with a first user, a first audio input. Inparticular embodiments, the assistant system may generate a plurality oftranscriptions corresponding to the first audio input based on aplurality of automatic speech recognition (ASR) engines. Each ASR enginemay be associated with a respective domain of a plurality of domains. Inparticular embodiments, the assistant system may determine, for eachtranscription, a combination of one or more intents and one or moreslots to be associated with the transcription. In particularembodiments, the assistant system may select, by a meta-speech engine,one or more combinations of intents and slots from the plurality ofcombinations to be associated with the first user input. In particularembodiments, the assistant system may generate a response to the firstaudio input based on the selected combinations. In particularembodiments, the assistant system may send, to the client system,instructions for presenting the response to the first audio input.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of server-side processes ofthe assistant system.

FIG. 4 illustrates an example diagram flow of processing a user input bythe assistant system.

FIG. 5 illustrates an example diagram flow of using a plurality ofautomatic speech recognition engines to generate transcriptions of anaudio input.

FIG. 6 illustrates an example diagram flow of using a plurality ofselection strategies to select a combination of intents and slots togenerate a response.

FIG. 7 illustrates an example mapping of automatic speech recognitionengines to a domain and its corresponding agents and available tasks.

FIG. 8 illustrates an example process of generating transcriptions foran audio input using a plurality of automatic speech recognitionengines.

FIG. 9 illustrates an example method for generating transcriptions foran audio input using a plurality of automatic speech recognitionengines.

FIG. 10 illustrates an example social graph.

FIG. 11 illustrates an example view of an embedding space.

FIG. 12 illustrates an example artificial neural network.

FIG. 13 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. patentapplication Ser. No. 16/153,574, filed 5 Oct. 2018, U.S. Design patentapplication No. 29/631910, filed 3 Jan. 2018, U.S. Design patentapplication No. 29/631747, filed 2 Jan. 2018, U.S. Design patentapplication No. 29/631913, filed 3 Jan. 2018, and U.S. Design patentapplication No. 29/631914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, orientation, etc. The assistant application136 may communicate the user input to the assistant system 140. Based onthe user input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality) by speaking into a microphone of the clientsystem 130. The assistant application 136 may then communicate therequest to the assistant system 140. The assistant system 140 mayaccordingly generate a response and send it back to the assistantapplication 136. The assistant application 136 may further present theresponse to the user in text and/or images on a display of the clientsystem 130.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, an assistant system 140, or a third-party system 170 tomanage, retrieve, modify, add, or delete, the information stored in datastore 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects. In particularembodiments, a third-party content provider may use one or morethird-party agents to provide content objects and/or services. Athird-party agent may be an implementation that is hosted and executingon the third-party system 170.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow, forexample, an assistant system 140 or a third-party system 170 to accessinformation from the social-networking system 160 by calling one or moreAPIs. An action logger may be used to receive communications from a webserver about a user's actions on or off the social-networking system160. In conjunction with the action log, a third-party-content-objectlog may be maintained of user exposures to third-party-content objects.A notification controller may provide information regarding contentobjects to a client system 130. Information may be pushed to a clientsystem 130 as notifications, or information may be pulled from a clientsystem 130 responsive to a request received from a client system 130.Authorization servers may be used to enforce one or more privacysettings of the users of the social-networking system 160. A privacysetting of a user determines how particular information associated witha user can be shared. The authorization server may allow users to opt into or opt out of having their actions logged by the social-networkingsystem 160 or shared with other systems (e.g., a third-party system170), such as, for example, by setting appropriate privacy settings.Third-party-content-object stores may be used to store content objectsreceived from third parties, such as a third-party system 170. Locationstores may be used for storing location information received from clientsystems 130 associated with users. Advertisement-pricing modules maycombine social information, the current time, location information, orother suitable information to provide relevant advertisements, in theform of notifications, to a user.

Assistant Systems

FIG. 2 illustrates an example architecture of an assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video, motion) in stateful and multi-turn conversations toget assistance. As an example and not by way of limitation, theassistant system 140 may support both audio input (verbal) and nonverbalinput, such as vision, location, gesture, motion, or hybrid/multi-modalinput. The assistant system 140 may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system 140 may analyzethe user input using natural-language understanding. The analysis may bebased on the user profile of the user for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation. Through the interaction with the user, theassistant system 140 may use dialog management techniques to manage andforward the conversation flow with the user. In particular embodiments,the assistant system 140 may further assist the user to effectively andefficiently digest the obtained information by summarizing theinformation. The assistant system 140 may also assist the user to bemore engaging with an online social network by providing tools that helpthe user interact with the online social network (e.g., creating posts,comments, messages). The assistant system 140 may additionally assistthe user to manage different tasks such as keeping track of events. Inparticular embodiments, the assistant system 140 may proactivelyexecute, without a user input, pre-authorized tasks that are relevant touser interests and preferences based on the user profile, at a timerelevant for the user. In particular embodiments, the assistant system140 may check privacy settings to ensure that accessing a user's profileor other user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. patentapplication Ser. No. 16/182,542, filed 6 Nov. 2018, which isincorporated by reference.

In particular embodiments, the assistant system 140 may assist the uservia a hybrid architecture built upon both client-side processes andserver-side processes. The client-side processes and the server-sideprocesses may be two parallel workflows for processing a user input andproviding assistances to the user. In particular embodiments, theclient-side processes may be performed locally on a client system 130associated with a user. By contrast, the server-side processes may beperformed remotely on one or more computing systems. In particularembodiments, an assistant orchestrator on the client system 130 maycoordinate receiving user input (e.g., audio signal) and determinewhether to use client-side processes, server-side processes, or both, torespond to the user input. A dialog arbitrator may analyze theprocessing results from each process. The dialog arbitrator may instructagents on the client-side or server-side to execute tasks associatedwith the user input based on the aforementioned analyses. The executionresults may be further rendered as output to the client system 130. Byleveraging both client-side and server-side processes, the assistantsystem 140 can effectively assist a user with optimal usage of computingresources while at the same time protecting user privacy and enhancingsecurity.

In particular embodiments, the assistant system 140 may receive a userinput from a client system 130 associated with the user. In particularembodiments, the user input may be a user-generated input that is sentto the assistant system 140 in a single turn. The user input may beverbal, nonverbal, or a combination thereof. As an example and not byway of limitation, the nonverbal user input may be based on the user'svoice, vision, location, activity, gesture, motion, or a combinationthereof. If the user input is based on the user's voice (e.g., the usermay speak to the client system 130), such user input may be firstprocessed by a system audio API 202 (application programming interface).The system audio API 202 may conduct echo cancellation, noise removal,beam forming, and self-user voice activation, speaker identification,voice activity detection (VAD), and any other acoustic techniques togenerate audio data that is readily processable by the assistant system140. In particular embodiments, the system audio API 202 may performwake-word detection 204 from the user input. As an example and not byway of limitation, a wake-word may be “hey assistant”. If such wake-wordis detected, the assistant system 140 may be activated accordingly. Inalternative embodiments, the user may activate the assistant system 140via a visual signal without a wake-word. The visual signal may bereceived at a low-power sensor (e.g., a camera) that can detect variousvisual signals. As an example and not by way of limitation, the visualsignal may be a barcode, a QR code or a universal product code (UPC)detected by the client system 130. As another example and not by way oflimitation, the visual signal may be the user's gaze at an object. Asyet another example and not by way of limitation, the visual signal maybe a user gesture, e.g., the user pointing at an object.

In particular embodiments, the audio data from the system audio API 202may be sent to an assistant orchestrator 206. The assistant orchestrator206 may be executing on the client system 130. In particularembodiments, the assistant orchestrator 206 may determine whether torespond to the user input by using client-side processes, server-sideprocesses, or both. As indicated in FIG. 2, the client-side processesare illustrated below the dashed line 207 whereas the server-sideprocesses are illustrated above the dashed line 207. The assistantorchestrator 206 may also determine to respond to the user input byusing both the client-side processes and the server-side processessimultaneously. Although FIG. 2 illustrates the assistant orchestrator206 as being a client-side process, the assistant orchestrator 206 maybe a server-side process or may be a hybrid process split betweenclient- and server-side processes.

In particular embodiments, the server-side processes may be as followsafter audio data is generated from the system audio API 202. Theassistant orchestrator 206 may send the audio data to a remote computingsystem that hosts different modules of the assistant system 140 torespond to the user input. In particular embodiments, the audio data maybe received at a remote automatic speech recognition (ASR) module 208.The ASR module 208 may allow a user to dictate and have speechtranscribed as written text, have a document synthesized as an audiostream, or issue commands that are recognized as such by the system. TheASR module 208 may use statistical models to determine the most likelysequences of words that correspond to a given portion of speech receivedby the assistant system 140 as audio input. The models may include oneor more of hidden Markov models, neural networks, deep learning models,or any combination thereof. The received audio input may be encoded intodigital data at a particular sampling rate (e.g., 16, 44.1, or 96 kHz)and with a particular number of bits representing each sample (e.g., 8,16, of 24 bits).

In particular embodiments, the ASR module 208 may comprise differentcomponents. The ASR module 208 may comprise one or more of agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized acoustic model, a personalized language model (PLM), or anend-pointing model. In particular embodiments, the G2P model may be usedto determine a user's grapheme-to-phoneme style, e.g., what it may soundlike when a particular user speaks a particular word. The personalizedacoustic model may be a model of the relationship between audio signalsand the sounds of phonetic units in the language. Therefore, suchpersonalized acoustic model may identify how a user's voice sounds. Thepersonalized acoustical model may be generated using training data suchas training speech received as audio input and the correspondingphonetic units that correspond to the speech. The personalizedacoustical model may be trained or refined using the voice of aparticular user to recognize that user's speech. In particularembodiments, the personalized language model may then determine the mostlikely phrase that corresponds to the identified phonetic units for aparticular audio input. The personalized language model may be a modelof the probabilities that various word sequences may occur in thelanguage. The sounds of the phonetic units in the audio input may bematched with word sequences using the personalized language model, andgreater weights may be assigned to the word sequences that are morelikely to be phrases in the language. The word sequence having thehighest weight may be then selected as the text that corresponds to theaudio input. In particular embodiments, the personalized language modelmay be also used to predict what words a user is most likely to saygiven a context. In particular embodiments, the end-pointing model maydetect when the end of an utterance is reached.

In particular embodiments, the output of the ASR module 208 may be sentto a remote natural-language understanding (NLU) module 210. The NLUmodule 210 may perform named entity resolution (NER). The NLU module 210may additionally consider contextual information when analyzing the userinput. In particular embodiments, an intent and/or a slot may be anoutput of the NLU module 210. An intent may be an element in apre-defined taxonomy of semantic intentions, which may indicate apurpose of a user interacting with the assistant system 140. The NLUmodule 210 may classify a user input into a member of the pre-definedtaxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 210may classify the input as having the intent [IN:play_music]. Inparticular embodiments, a domain may denote a social context ofinteraction, e.g., education, or a namespace for a set of intents, e.g.,music. A slot may be a named sub-string corresponding to a characterstring within the user input, representing a basic semantic entity. Forexample, a slot for “pizza” may be [SL:dish]. In particular embodiments,a set of valid or expected named slots may be conditioned on theclassified intent. As an example and not by way of limitation, for theintent [IN:play_music], a valid slot may be [SL:song_name]. Inparticular embodiments, the NLU module 210 may additionally extractinformation from one or more of a social graph, a knowledge graph, or aconcept graph, and retrieve a user's profile from one or more remotedata stores 212. The NLU module 210 may further process information fromthese different sources by determining what information to aggregate,annotating n-grams of the user input, ranking the n-grams withconfidence scores based on the aggregated information, and formulatingthe ranked n-grams into features that can be used by the NLU module 210for understanding the user input.

In particular embodiments, the NLU module 210 may identify one or moreof a domain, an intent, or a slot from the user input in a personalizedand context-aware manner. As an example and not by way of limitation, auser input may comprise “show me how to get to the coffee shop”. The NLUmodule 210 may identify the particular coffee shop that the user wantsto go based on the user's personal information and the associatedcontextual information. In particular embodiments, the NLU module 210may comprise a lexicon of a particular language and a parser and grammarrules to partition sentences into an internal representation. The NLUmodule 210 may also comprise one or more programs that perform naivesemantics or stochastic semantic analysis to the use of pragmatics tounderstand a user input. In particular embodiments, the parser may bebased on a deep learning architecture comprising multiple long-shortterm memory (LSTM) networks. As an example and not by way of limitation,the parser may be based on a recurrent neural network grammar (RNNG)model, which is a type of recurrent and recursive LSTM algorithm. Moreinformation on natural-language understanding may be found in U.S.patent application Ser. No. 16/011,062, filed 18 Jun. 2018, U.S. patentapplication Ser. No. 16/025,317, filed 2 Jul. 2018, and U.S. patentapplication Ser. No. 16/038,120, filed 17 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the output of the NLU module 210 may be sentto a remote reasoning module 214. The reasoning module 214 may comprisea dialog manager and an entity resolution component. In particularembodiments, the dialog manager may have complex dialog logic andproduct-related business logic. The dialog manager may manage the dialogstate and flow of the conversation between the user and the assistantsystem 140. The dialog manager may additionally store previousconversations between the user and the assistant system 140. Inparticular embodiments, the dialog manager may communicate with theentity resolution component to resolve entities associated with the oneor more slots, which supports the dialog manager to advance the flow ofthe conversation between the user and the assistant system 140. Inparticular embodiments, the entity resolution component may access oneor more of the social graph, the knowledge graph, or the concept graphwhen resolving the entities. Entities may include, for example, uniqueusers or concepts, each of which may have a unique identifier (ID). Asan example and not by way of limitation, the knowledge graph maycomprise a plurality of entities. Each entity may comprise a singlerecord associated with one or more attribute values. The particularrecord may be associated with a unique entity identifier. Each recordmay have diverse values for an attribute of the entity. Each attributevalue may be associated with a confidence probability. A confidenceprobability for an attribute value represents a probability that thevalue is accurate for the given attribute. Each attribute value may bealso associated with a semantic weight. A semantic weight for anattribute value may represent how the value semantically appropriate forthe given attribute considering all the available information. Forexample, the knowledge graph may comprise an entity of a book “Alice'sAdventures”, which includes information that has been extracted frommultiple content sources (e.g., an online social network, onlineencyclopedias, book review sources, media databases, and entertainmentcontent sources), and then deduped, resolved, and fused to generate thesingle unique record for the knowledge graph. The entity may beassociated with a “fantasy” attribute value which indicates the genre ofthe book “Alice's Adventures”. More information on the knowledge graphmay be found in U.S. patent application Ser. No. 16/048,049, filed 27Jul. 2018, and U.S. patent application Ser. No. 16/048,101, filed 27Jul. 2018, each of which is incorporated by reference.

In particular embodiments, the entity resolution component may check theprivacy constraints to guarantee that the resolving of the entities doesnot violate privacy policies. As an example and not by way oflimitation, an entity to be resolved may be another user who specifiesin his/her privacy settings that his/her identity should not besearchable on the online social network, and thus the entity resolutioncomponent may not return that user's identifier in response to arequest. Based on the information obtained from the social graph, theknowledge graph, the concept graph, and the user profile, and subject toapplicable privacy policies, the entity resolution component maytherefore resolve the entities associated with the user input in apersonalized, context-aware, and privacy-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID) corresponding to a particular user (e.g., aunique username or user ID number). In particular embodiments, each ofthe resolved entities may be also associated with a confidence score.More information on resolving entities may be found in U.S. patentapplication Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048,072, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the dialog manager may conduct dialogoptimization and assistant state tracking. Dialog optimization is theproblem of using data to understand what the most likely branching in adialog should be. As an example and not by way of limitation, withdialog optimization the assistant system 140 may not need to confirm whoa user wants to call because the assistant system 140 has highconfidence that a person inferred based on dialog optimization would bevery likely whom the user wants to call. In particular embodiments, thedialog manager may use reinforcement learning for dialog optimization.Assistant state tracking aims to keep track of a state that changes overtime as a user interacts with the world and the assistant system 140interacts with the user. As an example and not by way of limitation,assistant state tracking may track what a user is talking about, whomthe user is with, where the user is, what tasks are currently inprogress, and where the user's gaze is at, etc., subject to applicableprivacy policies. In particular embodiments, the dialog manager may usea set of operators to track the dialog state. The operators may comprisethe necessary data and logic to update the dialog state. Each operatormay act as delta of the dialog state after processing an incomingrequest. In particular embodiments, the dialog manager may furthercomprise a dialog state tracker and an action selector. In alternativeembodiments, the dialog state tracker may replace the entity resolutioncomponent and resolve the references/mentions and keep track of thestate.

In particular embodiments, the reasoning module 214 may further conductfalse trigger mitigation. The goal of false trigger mitigation is todetect false triggers (e.g., wake-word) of assistance requests and toavoid generating false records when a user actually does not intend toinvoke the assistant system 140. As an example and not by way oflimitation, the reasoning module 214 may achieve false triggermitigation based on a nonsense detector. If the nonsense detectordetermines that a wake-word makes no sense at this point in theinteraction with the user, the reasoning module 214 may determine thatinferring the user intended to invoke the assistant system 140 may beincorrect. In particular embodiments, the output of the reasoning module214 may be sent a remote dialog arbitrator 216.

In particular embodiments, each of the ASR module 208, NLU module 210,and reasoning module 214 may access the remote data store 212, whichcomprises user episodic memories to determine how to assist a user moreeffectively. More information on episodic memories may be found in U.S.patent application Ser. No. 16/552,559, filed 27 Aug. 2019, which isincorporated by reference. The data store 212 may additionally store theuser profile of the user. The user profile of the user may comprise userprofile data including demographic information, social information, andcontextual information associated with the user. The user profile datamay also include user interests and preferences on a plurality oftopics, aggregated through conversations on news feed, search logs,messaging platforms, etc. The usage of a user profile may be subject toprivacy constraints to ensure that a user's information can be used onlyfor his/her benefit, and not shared with anyone else. More informationon user profiles may be found in U.S. patent application Ser. No.15/967,239, filed 30 Apr. 2018, which is incorporated by reference.

In particular embodiments, parallel to the aforementioned server-sideprocess involving the ASR module 208, NLU module 210, and reasoningmodule 214, the client-side process may be as follows. In particularembodiments, the output of the assistant orchestrator 206 may be sent toa local ASR module 216 on the client system 130. The ASR module 216 maycomprise a personalized language model (PLM), a G2P model, and anend-pointing model. Because of the limited computing power of the clientsystem 130, the assistant system 140 may optimize the personalizedlanguage model at run time during the client-side process. As an exampleand not by way of limitation, the assistant system 140 may pre-compute aplurality of personalized language models for a plurality of possiblesubjects a user may talk about. When a user requests assistance, theassistant system 140 may then swap these pre-computed language modelsquickly so that the personalized language model may be optimized locallyby the assistant system 140 at run time based on user activities. As aresult, the assistant system 140 may have a technical advantage ofsaving computational resources while efficiently determining what theuser may be talking about. In particular embodiments, the assistantsystem 140 may also re-learn user pronunciations quickly at run time.

In particular embodiments, the output of the ASR module 216 may be sentto a local NLU module 218. In particular embodiments, the NLU module 218herein may be more compact compared to the remote NLU module 210supported on the server-side. When the ASR module 216 and NLU module 218process the user input, they may access a local assistant memory 220.The local assistant memory 220 may be different from the user memoriesstored on the data store 212 for the purpose of protecting user privacy.In particular embodiments, the local assistant memory 220 may be syncingwith the user memories stored on the data store 212 via the network 110.As an example and not by way of limitation, the local assistant memory220 may sync a calendar on a user's client system 130 with a server-sidecalendar associate with the user. In particular embodiments, any secureddata in the local assistant memory 220 may be only accessible to themodules of the assistant system 140 that are locally executing on theclient system 130.

In particular embodiments, the output of the NLU module 218 may be sentto a local reasoning module 222. The reasoning module 222 may comprise adialog manager and an entity resolution component. Due to the limitedcomputing power, the reasoning module 222 may conduct on-device learningthat is based on learning algorithms particularly tailored for clientsystems 130. As an example and not by way of limitation, federatedlearning may be used by the reasoning module 222. Federated learning isa specific category of distributed machine learning approaches whichtrains machine learning models using decentralized data residing on enddevices such as mobile phones. In particular embodiments, the reasoningmodule 222 may use a particular federated learning model, namelyfederated user representation learning, to extend existingneural-network personalization techniques to federated learning.Federated user representation learning can personalize models infederated learning by learning task-specific user representations (i.e.,embeddings) or by personalizing model weights. Federated userrepresentation learning is a simple, scalable, privacy-preserving, andresource-efficient. Federated user representation learning may dividemodel parameters into federated and private parameters. Privateparameters, such as private user embeddings, may be trained locally on aclient system 130 instead of being transferred to or averaged on aremote server. Federated parameters, by contrast, may be trainedremotely on the server. In particular embodiments, the reasoning module222 may use another particular federated learning model, namely activefederated learning to transmit a global model trained on the remoteserver to client systems 130 and calculate gradients locally on theseclient systems 130. Active federated learning may enable the reasoningmodule to minimize the transmission costs associated with downloadingmodels and uploading gradients. For active federated learning, in eachround client systems are selected not uniformly at random, but with aprobability conditioned on the current model and the data on the clientsystems to maximize efficiency. In particular embodiments, the reasoningmodule 222 may use another particular federated learning model, namelyfederated Adam. Conventional federated learning model may use stochasticgradient descent (SGD) optimizers. By contrast, the federated Adam modelmay use moment-based optimizers. Instead of using the averaged modeldirectly as what conventional work does, federated Adam model may usethe averaged model to compute approximate gradients. These gradients maybe then fed into the federated Adam model, which may de-noise stochasticgradients and use a per-parameter adaptive learning rate. Gradientsproduced by federated learning may be even noisier than stochasticgradient descent (because data may be not independent and identicallydistributed), so federated Adam model may help even more deal with thenoise. The federated Adam model may use the gradients to take smartersteps towards minimizing the objective function. The experiments showthat conventional federated learning on a benchmark has 1.6% drop in ROC(Receiver Operating Characteristics) curve whereas federated Adam modelhas only 0.4% drop. In addition, federated Adam model has no increase incommunication or on-device computation. In particular embodiments, thereasoning module 222 may also perform false trigger mitigation. Thisfalse trigger mitigation may help detect false activation requests,e.g., wake-word, on the client system 130 when the user's speech inputcomprises data that is subject to privacy constraints. As an example andnot by way of limitation, when a user is in a voice call, the user'sconversation is private and the false trigger detection based on suchconversation can only occur locally on the user's client system 130.

In particular embodiments, the assistant system 140 may comprise a localcontext engine 224. The context engine 224 may process all the otheravailable signals to provide more informative cues to the reasoningmodule 222. As an example and not by way of limitation, the contextengine 224 may have information related to people, sensory data fromclient system 130 sensors (e.g., microphone, camera) that are furtheranalyzed by computer vision technologies, geometry constructions,activity data, inertial data (e.g., collected by a VR headset),location, etc. In particular embodiments, the computer visiontechnologies may comprise human skeleton reconstruction, face detection,facial recognition, hand tracking, eye tracking, etc. In particularembodiments, geometry constructions may comprise constructing objectssurrounding a user using data collected by a client system 130. As anexample and not by way of limitation, the user may be wearing AR glassesand geometry construction may aim to determine where the floor is, wherethe wall is, where the user's hands are, etc. In particular embodiments,inertial data may be data associated with linear and angular motions. Asan example and not by way of limitation, inertial data may be capturedby AR glasses which measures how a user's body parts move.

In particular embodiments, the output of the local reasoning module 222may be sent to the dialog arbitrator 216. The dialog arbitrator 216 mayfunction differently in three scenarios. In the first scenario, theassistant orchestrator 206 determines to use server-side process, forwhich the dialog arbitrator 216 may transmit the output of the reasoningmodule 214 to a remote action execution module 226. In the secondscenario, the assistant orchestrator 206 determines to use bothserver-side processes and client-side processes, for which the dialogarbitrator 216 may aggregate output from both reasoning modules (i.e.,remote reasoning module 214 and local reasoning module 222) of bothprocesses and analyze them. As an example and not by way of limitation,the dialog arbitrator 216 may perform ranking and select the bestreasoning result for responding to the user input. In particularembodiments, the dialog arbitrator 216 may further determine whether touse agents on the server-side or on the client-side to execute relevanttasks based on the analysis. In the third scenario, the assistantorchestrator 206 determines to use client-side processes and the dialogarbitrator 216 needs to evaluate the output of the local reasoningmodule 222 to determine if the client-side processes can complete thetask of handling the user input.

In particular embodiments, for the first and second scenarios mentionedabove, the dialog arbitrator 216 may determine that the agents on theserver-side are necessary to execute tasks responsive to the user input.Accordingly, the dialog arbitrator 216 may send necessary informationregarding the user input to the action execution module 226. The actionexecution module 226 may call one or more agents to execute the tasks.In alternative embodiments, the action selector of the dialog managermay determine actions to execute and instruct the action executionmodule 226 accordingly. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. In particular embodiments,the agents may comprise first-party agents and third-party agents. Inparticular embodiments, first-party agents may comprise internal agentsthat are accessible and controllable by the assistant system 140 (e.g.agents associated with services provided by the online social network,such as messaging services or photo-share services). In particularembodiments, third-party agents may comprise external agents that theassistant system 140 has no control over (e.g., third-party online musicapplication agents, ticket sales agents). The first-party agents may beassociated with first-party providers that provide content objectsand/or services hosted by the social-networking system 160. Thethird-party agents may be associated with third-party providers thatprovide content objects and/or services hosted by the third-party system170. In particular embodiments, each of the first-party agents orthird-party agents may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, for the second and third scenarios mentionedabove, the dialog arbitrator 216 may determine that the agents on theclient-side are capable of executing tasks responsive to the user inputbut additional information is needed (e.g., response templates) or thatthe tasks can be only handled by the agents on the server-side. If thedialog arbitrator 216 determines that the tasks can be only handled bythe agents on the server-side, the dialog arbitrator 216 may sendnecessary information regarding the user input to the action executionmodule 226. If the dialog arbitrator 216 determines that the agents onthe client-side are capable of executing tasks but response templatesare needed, the dialog arbitrator 216 may send necessary informationregarding the user input to a remote response template generation module228. The output of the response template generation module 228 may befurther sent to a local action execution module 230 executing on theclient system 130.

In particular embodiments, the action execution module 230 may calllocal agents to execute tasks. A local agent on the client system 130may be able to execute simpler tasks compared to an agent on theserver-side. As an example and not by way of limitation, multipledevice-specific implementations (e.g., real-time calls for a clientsystem 130 or a messaging application on the client system 130) may behandled internally by a single agent. Alternatively, thesedevice-specific implementations may be handled by multiple agentsassociated with multiple domains. In particular embodiments, the actionexecution module 230 may additionally perform a set of generalexecutable dialog actions. The set of executable dialog actions mayinteract with agents, users and the assistant system 140 itself. Thesedialog actions may comprise dialog actions for slot request,confirmation, disambiguation, agent execution, etc. The dialog actionsmay be independent of the underlying implementation of the actionselector or dialog policy. Both tree-based policy and model-based policymay generate the same basic dialog actions, with a callback functionhiding any action selector specific implementation details.

In particular embodiments, the output from the remote action executionmodule 226 on the server-side may be sent to a remote response executionmodule 232. In particular embodiments, the action execution module 226may communicate back to the dialog arbitrator 216 for more information.The response execution module 232 may be based on a remoteconversational understanding (CU) composer. In particular embodiments,the output from the action execution module 226 may be formulated as a<k, c, u, d> tuple, in which k indicates a knowledge source, c indicatesa communicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer may comprisea natural-language generation (NLG) module and a user interface (UI)payload generator. The natural-language generator may generate acommunication content based on the output of the action execution module226 using different language models and/or language templates. Inparticular embodiments, the generation of the communication content maybe application specific and also personalized for each user. The CUcomposer may also determine a modality of the generated communicationcontent using the UI payload generator. In particular embodiments, theNLG module may comprise a content determination component, a sentenceplanner, and a surface realization component. The content determinationcomponent may determine the communication content based on the knowledgesource, communicative goal, and the user's expectations. As an exampleand not by way of limitation, the determining may be based on adescription logic. The description logic may comprise, for example,three fundamental notions which are individuals (representing objects inthe domain), concepts (describing sets of individuals), and roles(representing binary relations between individuals or concepts). Thedescription logic may be characterized by a set of constructors thatallow the natural-language generator to build complex concepts/rolesfrom atomic ones. In particular embodiments, the content determinationcomponent may perform the following tasks to determine the communicationcontent. The first task may comprise a translation task, in which theinput to the natural-language generator may be translated to concepts.The second task may comprise a selection task, in which relevantconcepts may be selected among those resulted from the translation taskbased on the user model. The third task may comprise a verificationtask, in which the coherence of the selected concepts may be verified.The fourth task may comprise an instantiation task, in which theverified concepts may be instantiated as an executable file that can beprocessed by the natural-language generator. The sentence planner maydetermine the organization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer may check privacyconstraints associated with the user to make sure the generation of thecommunication content follows the privacy policies. More information onnatural-language generation may be found in U.S. patent application Ser.No. 15/967,279, filed 30 Apr. 2018, and U.S. patent application Ser. No.15/966,455, filed 30 Apr. 2018, each of which is incorporated byreference.

In particular embodiments, the output from the local action executionmodule 230 on the client system 130 may be sent to a local responseexecution module 234. The response execution module 234 may be based ona local conversational understanding (CU) composer. The CU composer maycomprise a natural-language generation (NLG) module. As the computingpower of a client system 130 may be limited, the NLG module may besimple for the consideration of computational efficiency. Because theNLG module may be simple, the output of the response execution module234 may be sent to a local response expansion module 236. The responseexpansion module 236 may further expand the result of the responseexecution module 234 to make a response more natural and contain richersemantic information.

In particular embodiments, if the user input is based on audio signals,the output of the response execution module 232 on the server-side maybe sent to a remote text-to-speech (TTS) module 238. Similarly, theoutput of the response expansion module 236 on the client-side may besent to a local TTS module 240. Both TTS modules may convert a responseto audio signals. In particular embodiments, the output from theresponse execution module 232, the response expansion module 236, or theTTS modules on both sides, may be finally sent to a local render outputmodule 242. The render output module 242 may generate a response that issuitable for the client system 130. As an example and not by way oflimitation, the output of the response execution module 232 or theresponse expansion module 236 may comprise one or more ofnatural-language strings, speech, actions with parameters, or renderedimages or videos that can be displayed in a VR headset or AR smartglasses. As a result, the render output module 242 may determine whattasks to perform based on the output of CU composer to render theresponse appropriately for displaying on the VR headset or AR smartglasses. For example, the response may be visual-based modality (e.g.,an image or a video clip) that can be displayed via the VR headset or ARsmart glasses. As another example, the response may be audio signalsthat can be played by the user via VR headset or AR smart glasses. Asyet another example, the response may be augmented-reality data that canbe rendered VR headset or AR smart glasses for enhancing userexperience.

In particular embodiments, the assistant system 140 may have a varietyof capabilities including audio cognition, visual cognition, signalsintelligence, reasoning, and memories. In particular embodiments, thecapability of audio recognition may enable the assistant system 140 tounderstand a user's input associated with various domains in differentlanguages, understand a conversation and be able to summarize it,perform on-device audio cognition for complex commands, identify a userby voice, extract topics from a conversation and auto-tag sections ofthe conversation, enable audio interaction without a wake-word, filterand amplify user voice from ambient noise and conversations, understandwhich client system 130 (if multiple client systems 130 are in vicinity)a user is talking to.

In particular embodiments, the capability of visual cognition may enablethe assistant system 140 to perform face detection and tracking,recognize a user, recognize most people of interest in majormetropolitan areas at varying angles, recognize majority of interestingobjects in the world through a combination of existing machine-learningmodels and one-shot learning, recognize an interesting moment andauto-capture it, achieve semantic understanding over multiple visualframes across different episodes of time, provide platform support foradditional capabilities in people, places, objects recognition,recognize full set of settings and micro-locations includingpersonalized locations, recognize complex activities, recognize complexgestures to control a client system 130, handle images/videos fromegocentric cameras (e.g., with motion, capture angles, resolution,etc.), accomplish similar level of accuracy and speed regarding imageswith lower resolution, conduct one-shot registration and recognition ofpeople, places, and objects, and perform visual recognition on a clientsystem 130.

In particular embodiments, the assistant system 140 may leveragecomputer vision techniques to achieve visual cognition. Besides computervision techniques, the assistant system 140 may explore options that cansupplement these techniques to scale up the recognition of objects. Inparticular embodiments, the assistant system 140 may use supplementalsignals such as optical character recognition (OCR) of an object'slabels, GPS signals for places recognition, signals from a user's clientsystem 130 to identify the user. In particular embodiments, theassistant system 140 may perform general scene recognition (home, work,public space, etc.) to set context for the user and reduce thecomputer-vision search space to identify top likely objects or people.In particular embodiments, the assistant system 140 may guide users totrain the assistant system 140. For example, crowdsourcing may be usedto get users to tag and help the assistant system 140 recognize moreobjects over time. As another example, users can register their personalobjects as part of initial setup when using the assistant system 140.The assistant system 140 may further allow users to providepositive/negative signals for objects they interact with to train andimprove personalized models for them.

In particular embodiments, the capability of signals intelligence mayenable the assistant system 140 to determine user location, understanddate/time, determine family locations, understand users' calendars andfuture desired locations, integrate richer sound understanding toidentify setting/context through sound alone, build signals intelligencemodels at run time which may be personalized to a user's individualroutines.

In particular embodiments, the capability of reasoning may enable theassistant system 140 to have the ability to pick up any previousconversation threads at any point in the future, synthesize all signalsto understand micro and personalized context, learn interaction patternsand preferences from users' historical behavior and accurately suggestinteractions that they may value, generate highly predictive proactivesuggestions based on micro-context understanding, understand whatcontent a user may want to see at what time of a day, understand thechanges in a scene and how that may impact the user's desired content.

In particular embodiments, the capabilities of memories may enable theassistant system 140 to remember which social connections a userpreviously called or interacted with, write into memory and query memoryat will (i.e., open dictation and auto tags), extract richer preferencesbased on prior interactions and long-term learning, remember a user'slife history, extract rich information from egocentric streams of dataand auto catalog, and write to memory in structured form to form richshort, episodic and long-term memories.

FIG. 3 illustrates an example diagram flow of server-side processes ofthe assistant system 140. In particular embodiments, a server-assistantservice module 301 may access a request manager 302 upon receiving auser request. In alternative embodiments, the user request may be firstprocessed by the remote ASR module 208 if the user request is based onaudio signals. In particular embodiments, the request manager 302 maycomprise a context extractor 303 and a conversational understandingobject generator (CU object generator) 304. The context extractor 303may extract contextual information associated with the user request. Thecontext extractor 303 may also update contextual information based onthe assistant application 136 executing on the client system 130. As anexample and not by way of limitation, the update of contextualinformation may comprise content items are displayed on the clientsystem 130. As another example and not by way of limitation, the updateof contextual information may comprise whether an alarm is set on theclient system 130. As another example and not by way of limitation, theupdate of contextual information may comprise whether a song is playingon the client system 130. The CU object generator 304 may generateparticular content objects relevant to the user request. The contentobjects may comprise dialog-session data and features associated withthe user request, which may be shared with all the modules of theassistant system 140. In particular embodiments, the request manager 302may store the contextual information and the generated content objectsin data store 212 which is a particular data store implemented in theassistant system 140.

In particular embodiments, the request manger 302 may send the generatedcontent objects to the remote NLU module 210. The NLU module 210 mayperform a plurality of steps to process the content objects. At step305, the NLU module 210 may generate a whitelist for the contentobjects. In particular embodiments, the whitelist may compriseinterpretation data matching the user request. At step 306, the NLUmodule 210 may perform a featurization based on the whitelist. At step307, the NLU module 210 may perform domain classification/selection onuser request based on the features resulted from the featurization toclassify the user request into predefined domains. The domainclassification/selection results may be further processed based on tworelated procedures. At step 308 a, the NLU module 210 may process thedomain classification/selection result using an intent classifier. Theintent classifier may determine the user's intent associated with theuser request. In particular embodiments, there may be one intentclassifier for each domain to determine the most possible intents in agiven domain. As an example and not by way of limitation, the intentclassifier may be based on a machine-learning model that may take thedomain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedintent. At step 308 b, the NLU module 210 may process the domainclassification/selection result using a meta-intent classifier. Themeta-intent classifier may determine categories that describe the user'sintent. In particular embodiments, intents that are common to multipledomains may be processed by the meta-intent classifier. As an exampleand not by way of limitation, the meta-intent classifier may be based ona machine-learning model that may take the domainclassification/selection result as input and calculate a probability ofthe input being associated with a particular predefined meta-intent. Atstep 309 a, the NLU module 210 may use a slot tagger to annotate one ormore slots associated with the user request. In particular embodiments,the slot tagger may annotate the one or more slots for the n-grams ofthe user request. At step 309 b, the NLU module 210 may use a meta slottagger to annotate one or more slots for the classification result fromthe meta-intent classifier. In particular embodiments, the meta slottagger may tag generic slots such as references to items (e.g., thefirst), the type of slot, the value of the slot, etc. As an example andnot by way of limitation, a user request may comprise “change 500dollars in my account to Japanese yen.” The intent classifier may takethe user request as input and formulate it into a vector. The intentclassifier may then calculate probabilities of the user request beingassociated with different predefined intents based on a vectorcomparison between the vector representing the user request and thevectors representing different predefined intents. In a similar manner,the slot tagger may take the user request as input and formulate eachword into a vector. The intent classifier may then calculateprobabilities of each word being associated with different predefinedslots based on a vector comparison between the vector representing theword and the vectors representing different predefined slots. The intentof the user may be classified as “changing money”. The slots of the userrequest may comprise “500”, “dollars”, “account”, and “Japanese yen”.The meta-intent of the user may be classified as “financial service”.The meta slot may comprise “finance”.

In particular embodiments, the NLU module 210 may comprise a semanticinformation aggregator 310. The semantic information aggregator 310 mayhelp the NLU module 210 improve the domain classification/selection ofthe content objects by providing semantic information. In particularembodiments, the semantic information aggregator 310 may aggregatesemantic information in the following way. The semantic informationaggregator 310 may first retrieve information from a user context engine315. In particular embodiments, the user context engine 315 may compriseoffline aggregators and an online inference service. The offlineaggregators may process a plurality of data associated with the userthat are collected from a prior time window. As an example and not byway of limitation, the data may include news feed posts/comments,interactions with news feed posts/comments, search history, etc., thatare collected during a predetermined timeframe (e.g., from a prior90-day window). The processing result may be stored in the user contextengine 315 as part of the user profile. The online inference service mayanalyze the conversational data associated with the user that arereceived by the assistant system 140 at a current time. The analysisresult may be stored in the user context engine 315 also as part of theuser profile. In particular embodiments, both the offline aggregatorsand online inference service may extract personalization features fromthe plurality of data. The extracted personalization features may beused by other modules of the assistant system 140 to better understanduser input. In particular embodiments, the semantic informationaggregator 310 may then process the retrieved information, i.e., a userprofile, from the user context engine 315 in the following steps. Atstep 311, the semantic information aggregator 310 may process theretrieved information from the user context engine 315 based onnatural-language processing (NLP). In particular embodiments, thesemantic information aggregator 310 may tokenize text by textnormalization, extract syntax features from text, and extract semanticfeatures from text based on NLP. The semantic information aggregator 310may additionally extract features from contextual information, which isaccessed from dialog history between a user and the assistant system140. The semantic information aggregator 310 may further conduct globalword embedding, domain-specific embedding, and/or dynamic embeddingbased on the contextual information. At step 312, the processing resultmay be annotated with entities by an entity tagger. Based on theannotations, the semantic information aggregator 310 may generatedictionaries for the retrieved information at step 313. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. At step 314, the semanticinformation aggregator 310 may rank the entities tagged by the entitytagger. In particular embodiments, the semantic information aggregator310 may communicate with different graphs 320 including one or more ofthe social graph, the knowledge graph, or the concept graph to extractontology data that is relevant to the retrieved information from theuser context engine 315. In particular embodiments, the semanticinformation aggregator 310 may aggregate the user profile, the rankedentities, and the information from the graphs 320. The semanticinformation aggregator 310 may then provide the aggregated informationto the NLU module 210 to facilitate the domain classification/selection.

In particular embodiments, the output of the NLU module 210 may be sentto the remote reasoning module 214. The reasoning module 214 maycomprise a co-reference component 325, an entity resolution component330, and a dialog manager 335. The output of the NLU module 210 may befirst received at the co-reference component 325 to interpret referencesof the content objects associated with the user request. In particularembodiments, the co-reference component 325 may be used to identify anitem to which the user request refers. The co-reference component 325may comprise reference creation 326 and reference resolution 327. Inparticular embodiments, the reference creation 326 may create referencesfor entities determined by the NLU module 210. The reference resolution327 may resolve these references accurately. As an example and not byway of limitation, a user request may comprise “find me the nearestgrocery store and direct me there”. The co-reference component 325 mayinterpret “there” as “the nearest grocery store”. In particularembodiments, the co-reference component 325 may access the user contextengine 315 and the dialog manager 335 when necessary to interpretreferences with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution component 330 to resolve relevantentities. The entity resolution component 330 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution component 330 may comprise domain entity resolution 331 andgeneric entity resolution 332. The domain entity resolution 331 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 320. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 332 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 320. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a particular brand of electric car, the generic entity resolution 332may resolve the referenced brand of electric car as vehicle and thedomain entity resolution 331 may resolve the referenced brand ofelectric car as electric car.

In particular embodiments, the output of the entity resolution component330 may be sent to the dialog manager 335 to advance the flow of theconversation with the user. The dialog manager 335 may be anasynchronous state machine that repeatedly updates the state and selectsactions based on the new state. The dialog manager 335 may comprisedialog intent resolution 336 and dialog state tracker 337. In particularembodiments, the dialog manager 335 may execute the selected actions andthen call the dialog state tracker 337 again until the action selectedrequires a user response, or there are no more actions to execute. Eachaction selected may depend on the execution result from previousactions. In particular embodiments, the dialog intent resolution 336 mayresolve the user intent associated with the current dialog session basedon dialog history between the user and the assistant system 140. Thedialog intent resolution 336 may map intents determined by the NLUmodule 210 to different dialog intents. The dialog intent resolution 336may further rank dialog intents based on signals from the NLU module210, the entity resolution component 330, and dialog history between theuser and the assistant system 140. In particular embodiments, instead ofdirectly altering the dialog state, the dialog state tracker 337 may bea side-effect free component and generate n-best candidates of dialogstate update operators that propose updates to the dialog state. Thedialog state tracker 337 may comprise intent resolvers containing logicto handle different types of NLU intent based on the dialog state andgenerate the operators. In particular embodiments, the logic may beorganized by intent handler, such as a disambiguation intent handler tohandle the intents when the assistant system 140 asks fordisambiguation, a confirmation intent handler that comprises the logicto handle confirmations, etc. Intent resolvers may combine the turnintent together with the dialog state to generate the contextual updatesfor a conversation with the user. A slot resolution component may thenrecursively resolve the slots in the update operators with resolutionproviders including the knowledge graph and domain agents. In particularembodiments, the dialog state tracker 337 may update/rank the dialogstate of the current dialog session. As an example and not by way oflimitation, the dialog state tracker 337 may update the dialog state as“completed” if the dialog session is over. As another example and not byway of limitation, the dialog state tracker 337 may rank the dialogstate based on a priority associated with it.

In particular embodiments, the reasoning module 214 may communicate withthe remote action execution module 226 and the dialog arbitrator 216,respectively. In particular embodiments, the dialog manager 335 of thereasoning module 214 may communicate with a task completion component340 of the action execution module 226 about the dialog intent andassociated content objects. In particular embodiments, the taskcompletion module 340 may rank different dialog hypotheses for differentdialog intents. The task completion module 340 may comprise an actionselector 341. In alternative embodiments, the action selector 341 may becomprised in the dialog manager 335. In particular embodiments, thedialog manager 335 may additionally check against dialog policies 345comprised in the dialog arbitrator 216 regarding the dialog state. Inparticular embodiments, a dialog policy 345 may comprise a datastructure that describes an execution plan of an action by an agent 350.The dialog policy 345 may comprise a general policy 346 and taskpolicies 347. In particular embodiments, the general policy 346 may beused for actions that are not specific to individual tasks. The generalpolicy 346 may comprise handling low confidence intents, internalerrors, unacceptable user response with retries, skipping or insertingconfirmation based on ASR or NLU confidence scores, etc. The generalpolicy 346 may also comprise the logic of ranking dialog state updatecandidates from the dialog state tracker 337 output and pick the one toupdate (such as picking the top ranked task intent). In particularembodiments, the assistant system 140 may have a particular interfacefor the general policy 346, which allows for consolidating scatteredcross-domain policy/business-rules, especial those found in the dialogstate tracker 337, into a function of the action selector 341. Theinterface for the general policy 346 may also allow for authoring ofself-contained sub-policy units that may be tied to specific situationsor clients, e.g., policy functions that may be easily switched on or offbased on clients, situation, etc. The interface for the general policy346 may also allow for providing a layering of policies with back-off,i.e. multiple policy units, with highly specialized policy units thatdeal with specific situations being backed up by more general policies346 that apply in wider circumstances. In this context the generalpolicy 346 may alternatively comprise intent or task specific policy. Inparticular embodiments, a task policy 347 may comprise the logic foraction selector 341 based on the task and current state. In particularembodiments, there may be the following four types of task policies347: 1) manually crafted tree-based dialog plans; 2) coded policy thatdirectly implements the interface for generating actions; 3)configurator-specified slot-filling tasks; and 4) machine-learning modelbased policy learned from data. In particular embodiments, the assistantsystem 140 may bootstrap new domains with rule-based logic and laterrefine the task policies 347 with machine-learning models. In particularembodiments, a dialog policy 345 may a tree-based policy, which is apre-constructed dialog plan. Based on the current dialog state, a dialogpolicy 345 may choose a node to execute and generate the correspondingactions. As an example and not by way of limitation, the tree-basedpolicy may comprise topic grouping nodes and dialog action (leaf) nodes.

In particular embodiments, the action selector 341 may take candidateoperators of dialog state and consult the dialog policy 345 to decidewhat action should be executed. The assistant system 140 may use ahierarchical dialog policy with general policy 346 handling thecross-domain business logic and task policies 347 handles thetask/domain specific logic. In particular embodiments, the generalpolicy 346 may pick one operator from the candidate operators to updatethe dialog state, followed by the selection of a user facing action by atask policy 347. Once a task is active in the dialog state, thecorresponding task policy 347 may be consulted to select right actions.In particular embodiments, both the dialog state tracker 337 and theaction selector 341 may not change the dialog state until the selectedaction is executed. This may allow the assistant system 140 to executethe dialog state tracker 337 and the action selector 341 for processingspeculative ASR results and to do n-best ranking with dry runs. Inparticular embodiments, the action selector 341 may take the dialogstate update operators as part of the input to select the dialog action.The execution of the dialog action may generate a set of expectation toinstruct the dialog state tracker 337 to handler future turns. Inparticular embodiments, an expectation may be used to provide context tothe dialog state tracker 337 when handling the user input from nextturn. As an example and not by way of limitation, slot request dialogaction may have the expectation of proving a value for the requestedslot.

In particular embodiments, the dialog manager 335 may support multi-turncompositional resolution of slot mentions. For a compositional parsefrom the NLU 210, the resolver may recursively resolve the nested slots.The dialog manager 335 may additionally support disambiguation for thenested slots. As an example and not by way of limitation, the userrequest may be “remind me to call Alex”. The resolver may need to knowwhich Alex to call before creating an actionable reminder to-do entity.The resolver may halt the resolution and set the resolution state whenfurther user clarification is necessary for a particular slot. Thegeneral policy 346 may examine the resolution state and createcorresponding dialog action for user clarification. In dialog statetracker 337, based on the user request and the last dialog action, thedialog manager may update the nested slot. This capability may allow theassistant system 140 to interact with the user not only to collectmissing slot values but also to reduce ambiguity of morecomplex/ambiguous utterances to complete the task. In particularembodiments, the dialog manager may further support requesting missingslots in a nested intent and multi-intent user requests (e.g., “takethis photo and send it to Dad”),In particular embodiments, the dialogmanager 335 may support machine-learning models for more robust dialogexperience. As an example and not by way of limitation, the dialog statetracker 337 may use neural network based models (or any other suitablemachine-learning models) to model belief over task hypotheses. Asanother example and not by way of limitation, for action selector 341,highest priority policy units may comprise white-list/black-listoverrides, which may have to occur by design; middle priority units maycomprise machine-learning models designed for action selection; andlower priority units may comprise rule-based fallbacks when themachine-learning models elect not to handle a situation. In particularembodiments, machine-learning model based general policy unit may helpthe assistant system 140 reduce redundant disambiguation or confirmationsteps, thereby reducing the number of turns to execute the user request.

In particular embodiments, the action execution module 226 may calldifferent agents 350 for task execution. An agent 350 may select amongregistered content providers to complete the action. The data structuremay be constructed by the dialog manager 335 based on an intent and oneor more slots associated with the intent. A dialog policy 345 mayfurther comprise multiple goals related to each other through logicaloperators. In particular embodiments, a goal may be an outcome of aportion of the dialog policy and it may be constructed by the dialogmanager 335. A goal may be represented by an identifier (e.g., string)with one or more named arguments, which parameterize the goal. As anexample and not by way of limitation, a goal with its associated goalargument may be represented as {confirm artist, args: {artist:“Artist_Name”}}. In particular embodiments, a dialog policy may be basedon a tree-structured representation, in which goals are mapped to leavesof the tree. In particular embodiments, the dialog manager 335 mayexecute a dialog policy 345 to determine the next action to carry out.The dialog policies 345 may comprise generic policy 346 and domainspecific policies 347, both of which may guide how to select the nextsystem action based on the dialog state. In particular embodiments, thetask completion component 340 of the action execution module 226 maycommunicate with dialog policies 345 comprised in the dialog arbitrator216 to obtain the guidance of the next system action. In particularembodiments, the action selection component 341 may therefore select anaction based on the dialog intent, the associated content objects, andthe guidance from dialog policies 345.

In particular embodiments, the output of the action execution module 226may be sent to the remote response execution module 232. Specifically,the output of the task completion component 340 of the action executionmodule 226 may be sent to the CU composer 355 of the response executionmodule 226. In alternative embodiments, the selected action may requireone or more agents 350 to be involved. As a result, the task completionmodule 340 may inform the agents 350 about the selected action.Meanwhile, the dialog manager 335 may receive an instruction to updatethe dialog state. As an example and not by way of limitation, the updatemay comprise awaiting agents' 350 response. In particular embodiments,the CU composer 355 may generate a communication content for the userusing a natural-language generation (NLG) module 356 based on the outputof the task completion module 340. In particular embodiments, the NLGmodule 356 may use different language models and/or language templatesto generate natural language outputs. The generation of natural languageoutputs may be application specific. The generation of natural languageoutputs may be also personalized for each user. The CU composer 355 mayalso determine a modality of the generated communication content usingthe UI payload generator 357. Since the generated communication contentmay be considered as a response to the user request, the CU composer 355may additionally rank the generated communication content using aresponse ranker 358. As an example and not by way of limitation, theranking may indicate the priority of the response.

In particular embodiments, the response execution module 232 may performdifferent tasks based on the output of the CU composer 355. These tasksmay include writing (i.e., storing/updating) the dialog state 361retrieved from data store 212 and generating responses 362. Inparticular embodiments, the output of CU composer 355 may comprise oneor more of natural-language strings, speech, actions with parameters, orrendered images or videos that can be displayed in a VR headset or ARsmart glass. As a result, the response execution module 232 maydetermine what tasks to perform based on the output of CU composer 355.In particular embodiments, the generated response and the communicationcontent may be sent to the local render output module 242 by theresponse execution module 232. In alternative embodiments, the output ofthe CU composer 355 may be additionally sent to the remote TTS module238 if the determined modality of the communication content is audio.The speech generated by the TTS module 238 and the response generated bythe response execution module 232 may be then sent to the render outputmodule 242.

FIG. 4 illustrates an example diagram flow of processing a user input bythe assistant system 140. As an example and not by way of limitation,the user input may be based on audio signals. In particular embodiments,a mic array 402 of the client system 130 may receive the audio signals(e.g., speech). The audio signals may be transmitted to a process loop404 in a format of audio frames. In particular embodiments, the processloop 404 may send the audio frames for voice activity detection (VAD)406 and wake-on-voice (WoV) detection 408. The detection results may bereturned to the process loop 404. If the WoV detection 408 indicates theuser wants to invoke the assistant system 140, the audio frames togetherwith the VAD 406 result may be sent to an encode unit 410 to generateencoded audio data. After encoding, the encoded audio data may be sentto an encrypt unit 412 for privacy and security purpose, followed by alink unit 414 and decrypt unit 416. After decryption, the audio data maybe sent to a mic driver 418, which may further transmit the audio datato an audio service module 420. In alternative embodiments, the userinput may be received at a wireless device (e.g., Bluetooth device)paired with the client system 130. Correspondingly, the audio data maybe sent from a wireless-device driver 422 (e.g., Bluetooth driver) tothe audio service module 420. In particular embodiments, the audioservice module 420 may determine that the user input can be fulfilled byan application executing on the client system 130. Accordingly, theaudio service module 420 may send the user input to a real-timecommunication (RTC) module 424. The RTC module 424 may deliver audiopackets to a video or audio communication system (e.g., VOIP or videocall). The RTC module 424 may call a relevant application (App) 426 toexecute tasks related to the user input.

In particular embodiments, the audio service module 420 may determinethat the user is requesting assistance that needs the assistant system140 to respond. Accordingly, the audio service module 420 may inform theclient-assistant service module 426. In particular embodiments, theclient-assistant service module 426 may communicate with the assistantorchestrator 206. The assistant orchestrator 206 may determine whetherto use client-side processes or server-side processes to respond to theuser input. In particular embodiments, the assistant orchestrator 206may determine to use client-side processes and inform theclient-assistant service module 426 about such decision. As a result,the client-assistant service module 426 may call relevant modules torespond to the user input.

In particular embodiments, the client-assistant service module 426 mayuse the local ASR module 216 to analyze the user input. The ASR module216 may comprise a grapheme-to-phoneme (G2P) model, a pronunciationlearning model, a personalized language model (PLM), an end-pointingmodel, and a personalized acoustic model. In particular embodiments, theclient-assistant service module 426 may further use the local NLU module218 to understand the user input. The NLU module 218 may comprise anamed entity resolution (NER) component and a contextual session-basedNLU component. In particular embodiments, the client-assistant servicemodule 426 may use an intent broker 428 to analyze the user's intent. Tobe accurate about the user's intent, the intent broker 428 may access anentity store 430 comprising entities associated with the user and theworld. In alternative embodiments, the user input may be submitted viaan application 432 executing on the client system 130. In this case, aninput manager 434 may receive the user input and analyze it by anapplication environment (App Env) module 436. The analysis result may besent to the application 432 which may further send the analysis resultto the ASR module 216 and NLU module 218. In alternative embodiments,the user input may be directly submitted to the client-assistant servicemodule 426 via an assistant application 438 executing on the clientsystem 130. Then the client-assistant service module 426 may performsimilar procedures based on modules as aforementioned, i.e., the ASRmodule 216, the NLU module 218, and the intent broker 428.

In particular embodiments, the assistant orchestrator 206 may determineto user server-side process. Accordingly, the assistant orchestrator 206may send the user input to one or more computing systems that hostdifferent modules of the assistant system 140. In particularembodiments, a server-assistant service module 301 may receive the userinput from the assistant orchestrator 206. The server-assistant servicemodule 301 may instruct the remote ASR module 208 to analyze the audiodata of the user input. The ASR module 208 may comprise agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized language model (PLM), an end-pointing model, and apersonalized acoustic model. In particular embodiments, theserver-assistant service module 301 may further instruct the remote NLUmodule 210 to understand the user input. In particular embodiments, theserver-assistant service module 301 may call the remote reasoning model214 to process the output from the ASR module 208 and the NLU module210. In particular embodiments, the reasoning model 214 may performentity resolution and dialog optimization. In particular embodiments,the output of the reasoning model 314 may be sent to the agent 350 forexecuting one or more relevant tasks.

In particular embodiments, the agent 350 may access an ontology module440 to accurately understand the result from entity resolution anddialog optimization so that it can execute relevant tasks accurately.The ontology module 440 may provide ontology data associated with aplurality of predefined domains, intents, and slots. The ontology datamay also comprise the structural relationship between different slotsand domains. The ontology data may further comprise information of howthe slots may be grouped, related within a hierarchy where the higherlevel comprises the domain, and subdivided according to similarities anddifferences. The ontology data may also comprise information of how theslots may be grouped, related within a hierarchy where the higher levelcomprises the topic, and subdivided according to similarities anddifferences. Once the tasks are executed, the agent 350 may return theexecution results together with a task completion indication to thereasoning module 214.

The embodiments disclosed herein may include or be implemented inconjunction with an artificial reality system. Artificial reality is aform of reality that has been adjusted in some manner beforepresentation to a user, which may include, e.g., a virtual reality (VR),an augmented reality (AR), a mixed reality (MR), a hybrid reality, orsome combination and/or derivatives thereof. Artificial reality contentmay include completely generated content or generated content combinedwith captured content (e.g., real-world photographs). The artificialreality content may include video, audio, haptic feedback, or somecombination thereof, and any of which may be presented in a singlechannel or in multiple channels (such as stereo video that produces athree-dimensional effect to the viewer). Additionally, in someembodiments, artificial reality may be associated with applications,products, accessories, services, or some combination thereof, that are,e.g., used to create content in an artificial reality and/or used in(e.g., perform activities in) an artificial reality. The artificialreality system that provides the artificial reality content may beimplemented on various platforms, including a head-mounted display (HMD)connected to a host computer system, a standalone HMD, a mobile deviceor computing system, or any other hardware platform capable of providingartificial reality content to one or more viewers.

Natural-Language Understanding Based Meta-Speech Systems

In particular embodiments, the assistant system 140 may utilize multipleautomatic speech recognition (ASR) engines to analyze an audio inputthrough a meta-speech engine. In particular embodiments, the ASR enginesmay be part of the ASR modules 208, 216. In order for an ASR engine tooperate with sufficient accuracy, the ASR engine may require a largevolume of training data to establish the foundation for the speechmodels that correspond to the ASR engines. As an example and not by wayof limitation, the large volume of training data may comprise 100,000audio inputs and their respective transcriptions. However, wheninitially training a speech model, there may not be a sufficient volumeof training data to build out the speech model with adequateoperability. That is, as an example and not by way of limitation, thespeech model may not have enough training data to be able to accuratelygenerate transcriptions for at least 95% of audio inputs. This may bedetermined if the user needs to repeat a request and/or if there is anerror in generating a transcription. Therefore, the speech model mayrequire a larger volume of training data in order to accurately generatethe transcriptions of a threshold number of audio inputs. On the otherhand, there may be ASR engines that have been trained on a limited dataset related to a limited set of tasks that operate with sufficientaccuracy (e.g., 95% of audio inputs are accurately transcribed) for thatlimited set of tasks. As an example and not by way of limitation, theremay be an ASR engine for messaging/calling, an ASR engine formusic-related functions, and an ASR engine for default systemoperations. For instance, the ASR engine for messaging/calling mayaccurately transcribe a threshold number of audio inputs (e.g., 95%)related to messaging/calling requests. As such, an assistant system 140may leverage the individual ASR engines in order to improve upon theaccuracy of ASR results. To do so, the assistant system 140 may receivean audio input and send the audio input to multiple ASR engines. Bysending the audio input to multiple ASR engines, each ASR engine maygenerate a transcription based on the respective speech model of the ASRengine. This improves upon the accuracy of the ASR results by increasingthe probability of an accurate transcription of the audio input. As anexample and not by way of limitation, if a user requests to play musicusing the assistant system 140, the audio input is sent to all of theavailable ASR engines, one of which is the ASR engine for music-relatedfunctions. The ASR engine for music-related functions may accuratelytranscribe the audio input to be the request to play music. By sendingthe audio input corresponding to the request to play music to an ASRengine for music-related functions, the assistant system 140 may improvethe accuracy of the transcription of the audio input because theparticular ASR engine may have a large volume of training datacorresponding to audio inputs associated with music-related functions.By using multiple ASR engines, the assistant system 140 may have arobust foundation of speech models to handle and transcribe differentrequests, such as music-related requests or messaging/calling-relatedrequests. The use of multiple ASR engines, each associated with theirown respective speech model, may help to avoid the need to extensivelytrain one speech model on a large dataset, such as training one speechmodel to transcribe a request to play music and also to transcribe arequest for weather information. These individual ASR engines mayalready be trained for their respective functions and may not need anyfurther training to achieve sufficient accuracy for operability fortheir respective functions. Therefore, the use of individual ASR enginesmay reduce or eliminate the time needed to train a speech model toachieve adequate operability (e.g., be able to accurately transcribe athreshold number of audio inputs) in handling/transcribing audio inputsthat are associated with a wide range of functions. The assistant system140 may take the output of the ASR engines and send that into a naturallanguage understanding (NLU) module that identifies one or more intentsand one or more slots associated with each output of the respective ASRengine. The output of the NLU module may be sent to a meta-speechengine, which may select one or more intents and one or more slotsassociated with the received audio input from the possible choices basedon a selection strategy. The selection strategy may be, for example, asimple selection strategy, a system combination with a machine learningmodel strategy, or a ranked strategy. After determining the intents andslots, the meta-speech engine may send the output to be processed by oneor more agents of the assistant system 140. In particular embodiments,the meta-speech engine may be a component of the assistant system 140that processes an audio input to generate a combination of intents andslots. In particular embodiments, the meta-speech engine may comprise aplurality of ASR engines and an NLU module. Although this disclosuredescribes utilizing multiple ASR engines to analyze an audio input in aparticular manner, this disclosure contemplates utilizing multiple ASRengines to analyze an audio input in any suitable manner.

In particular embodiments, the assistant system 140 may receive an audioinput from a client system 130. In particular embodiments, the clientsystem 130 may be associated with a user. As an example and not by wayof limitation, the audio input may be received from a smartphone of theuser, an assistant system of the user, or another computing device ofthe user. In particular embodiments, the user may speak to the clientsystem 130, which passes the audio input to the assistant system 140. Inparticular embodiments, the assistant system 140 may be a client system130 that receives audio input from the user. After receiving the audioinput, the assistant system 140 may pass the audio input to an assistantorchestrator that passes the audio input to be handled in a server-sideprocess. In particular embodiments, the assistant system 140 may haveone or more stored ASR engines that may handle the processing of theaudio input. These ASR engines may periodically be updated based onspeech models that are located remotely. As an example and not by way oflimitation, the ASR engines may be updated every month, week, day, etc.,based on speech models from a remote computing system. The ASR enginesmay also be updated based on when new updates are detected. There may beASR engines that are stored locally. Although this disclosure describesreceiving an audio input in a particular manner, this disclosurecontemplates receiving an audio input in any suitable manner.

In particular embodiments, the assistant system 140 may generate aplurality of transcriptions corresponding to the audio input. Thetranscription may be a text conversion of the audio input. In particularembodiments, the assistant system 140 may have a selection process ofwhich ASR engine to send the audio input for transcription. Inparticular embodiments, the assistant system 140 may use a plurality ofASR engines (e.g., send audio input to multiple ASR engines or allavailable ASR engines) to generate the plurality of transcriptions. TheASR engines may be stored locally, on a remote computing system, orboth. The assistant system 140 may use a combination of ASR enginesstored locally and/or on the remote computing system. In particularembodiments, the ASR engines may be a third-party ASR engine associatedwith a third-party system 170. The third-party ASR engines may beseparate from and external to the assistant system 140. In particularembodiments, one or more ASR engines may be added to be used by theassistant system 140. The additional ASR engines may be stored locallyand/or on a remote computing system. In particular embodiments, each ASRengine may be associated with a particular domain of a plurality ofdomains. As an example and not by way of limitation, an ASR engine forshopping-related functions may be associated with a shopping domain oran ASR engine for music-related functions may be associated with a musicdomain or a more general media domain. Each of the domains may compriseone or more agents specific to that domain. As an example and not by wayof limitation, a music domain may be associated with an online musicapplication agent. Each domain may be associated with a set of tasksspecific to that respective domain. As an example and not by way oflimitation, the tasks associated with a shopping domain may include apurchase task or an add-to-shopping-cart task. In particularembodiments, two or more discrete ASR engines may be combined togenerate a combined ASR engine. Each of these former discrete ASRengines may be associated with a separate domain. As an example and notby way of limitation, an ASR engine for music-related functions and anASR engine for video-related functions may be combined to generate anASR engine for media-related functions. Each of the ASR engines may beassociated with a set of agents of a plurality of agents. In particularembodiments, the agents may comprise one or more of a first-party agentor a third-party agent. In particular embodiments, domains may beassociated with a plurality of agents, each of which may be operable toexecute tasks specific to the domain. As an example and not by way oflimitation, a music domain may be associated with an online musicapplication agent that can play songs. In particular embodiments,generating the plurality of transcriptions may comprise sending theaudio input to each of the ASR engines and receiving the plurality oftranscriptions from each of the ASR engines. In particular embodiments,the assistant system 140 may send the audio input to a third-party ASRengine to generate one or more transcriptions. The third-party ASRengine may return the generated transcriptions. The assistant system 140may select from the received transcriptions to send to an NLU module todetermine the intents and slots associated with the transcription.Although this disclosure describes generating a plurality oftranscriptions corresponding to the audio input in a particular manner,this disclosure contemplates generating a plurality of transcriptionscorresponding to the audio input in any suitable manner.

In particular embodiments, the assistant system 140 may determine acombination of intents and slots to be associated with a transcription.The assistant system 140 may collect each generated transcription anddetermine one or more intents and one or more slots associated with thetranscription. As an example and not by way of limitation, for atranscription, “Where is the nearest gas station?” The assistant system140 may determine the intent is [IN:find_location] and the slot is[SL:gas_station]. In particular embodiments, the assistant system 140may send the transcriptions received from the ASR engines to the NLUmodule to determine one or more intents and one or more slots associatedwith the transcription. Although this disclosure describes determining acombination of intents and slots to be associated with a transcriptionin a particular manner, this disclosure contemplates determining acombination of intents and slots to be associated with a transcriptionin any suitable manner.

In particular embodiments, the assistant system 140 may select one ormore combinations of intents and slots from the plurality ofcombinations to be associated with the audio input. After generating theplurality of combinations of intents and slots from the plurality oftranscriptions, the assistant system 140 may select a combination to beassociated with the audio input to determine what the user requested. Asan example and not by way of limitation, the assistant system 140 maydetermine whether or not the audio input corresponds to a request toperform a search on the internet, play a song, and the like by selectingthe combination of intents and slots to be used to represent the audioinput. In particular embodiments, the assistant system 140 may use ameta-speech engine to select the one or more combinations of intents andslots. For instance, the meta-speech engine may perform the selectionprocess described herein. In particular embodiments, the assistantsystem 140 may identify a domain for each of the combinations of intentsand slots. As an example and not by way of limitation, an intent[IN:find_location] and slot [SL:grocery_store] may be associated with anavigation domain. The assistant system 140 may perform a mapping of thedomain of the combination of intents and slots to the domain associatedwith the plurality of ASR engines. As an example and not by way oflimitation, for two ASR engines, an ASR engine for shopping-relatedfunctions and an ASR engine for music-related functions, the assistantsystem 140 may map the domains of each combination of intents and slotsto each of the ASR engines. So if a combination of intents and slots isassociated with a shopping domain, then that particular combination maybe mapped to the ASR engine for shopping-related functions. Inparticular embodiments, if the combination of intents and slots aremapped to an ASR engine, then the assistant system 140 may select to usethe combination that is mapped to an ASR engine. If there are multiplemappings (e.g., multiple combinations to different ASR engines based ondomain), the assistant system 140 may select the combination that hasthe greatest number of mappings. As an example and not by way oflimitation, if three combinations were mapped to an ASR engine forshopping-related functions based on domain, but one combination wasmapped to an ASR engine for music-related functions based on domain,then the assistant system 140 may select to use the combinations thatwere mapped to the ASR engine for shopping-related functions.

In particular embodiments, there may be an ASR engine for default systemoperations. If a combination of intents and slots determined from atranscription generated by the ASR engine for default system operationsmaps to the domain of the ASR engine for default system operations, thenthat combination of intents and slots may be selected to be associatedwith the audio input. In particular embodiments, if there is a conflictor error, then the combination associated with the ASR engine fordefault system operations may be used. As an example and not by way oflimitation, the combination of intents and slots associated with thetranscription generated from the ASR engine for default systemoperations may be used to represent the audio input. In particularembodiments, the assistant system 140 may use a machine-learning modelto rank the combinations of intents and slots. The assistant system 140may identify features of the combinations of intents and slots, each ofthese features may indicate whether the combination has a particularattribute. As an example and not by way of limitation, a feature of thecombination may relate to if the combination references a location. Ifthe user is determined to be traveling (e.g., in a vehicle) the user maybe interested in directions and combinations that reference a locationmay be ranked higher than combinations that do not. The assistant system140 may select a combination based on the ranking of the combinations sothat a higher ranked combination may be selected to represent the audioinput. In particular embodiments, the assistant system 140 may identifysame combinations of intents and slots and rank the same combinations ofintents and slots based on the number of same combinations of intentsand slots. As an example and not by way of limitation, if an ASR enginefor general functions generates a transcription that is associated witha shopping domain combination of intents and slots and an ASR engine forshopping-related functions generates a transcription that is associatedwith the shopping domain combination of intents and slots, then theshopping domain combination of intents and slots may be ranked higherthan other combinations. Although this disclosure describes selectingone or more combinations of intents and slots from the plurality ofcombinations to be associated with the audio input in a particularmanner, this disclosure contemplates selecting one or more combinationsof intents and slots from the plurality of combinations to be associatedwith the audio input in any suitable manner.

In particular embodiments, the assistant system 140 may generate aresponse to the audio input based on the selected combination. Afterselecting the combination of intents and slots to be associated with theaudio input, the assistant system 140 may send the combination ofintents and slots to a reasoning module 222 to resolve the intents andslots and generate a response. As an example and not by way oflimitation, for the intent [IN:find_location] and slot [SL:gas_station],the assistant system 140 may identify the closest gas station or anumber of close gas stations. The assistant system 140 may generate aresponse that includes the list of gas stations for the user to view. Inparticular embodiments, the assistant system 140 may send the selectedcombination to a plurality of agents to resolve. The assistant system140 may receive a plurality of responses from each of the agents andrank the responses from the agents. The assistant system 140 may selecta response from the plurality of responses based on the ranking. Inparticular embodiments, the response may be an action to be performed bythe assistant system 140 or results generated from a query. As anexample and not by way of limitation, the user may have requested toturn on lights or requested information on the weather. Although thisdisclosure describes generating a response to an audio input in aparticular manner, this disclosure contemplates generating a response toan audio input in any suitable manner.

In particular embodiments, the assistant system 140 may sendinstructions to present a response to the audio input. The assistantsystem 140 may send instructions to the client system 130 to present theresponse to the audio input to the user. In particular embodiments, theinstructions for presenting the response comprises a notification of anaction to be performed or a list of one or more results. As an exampleand not by way of limitation, the assistant system 140 may sendinstructions to the client system 130 to perform an action and/or notifythe user an action has been completed. For instance if the user requeststo turn the lights on, the user may be notified that the lights havebeen turned on. As another example and not by way of limitation, if theuser requested weather information, the assistant system 140 may sendthe weather information to the client system 130 to present to the user.Although this disclosure describes sending instructions to present aresponse to the audio input in a particular manner, this disclosurecontemplates sending instructions to present a response to the audioinput in any suitable manner.

FIG. 5 illustrates an example diagram flow 500 of using a plurality ofautomatic speech recognition engines 208 to generate transcriptions ofan audio input. In particular embodiments, the client system 130 mayreceive an audio input from the user. The client system 130 may send theaudio input to an assistant orchestrator 206. The assistant orchestrator206 may send the audio input to the meta engine 502. The meta engine 502may be located on the client system 130, stored locally on an assistantsystem 140, or stored on a remote computing system. The meta engine 502may process the received audio input and send the audio input to aplurality of ASR engines 208. In particular embodiments, the ASR engines208 may be ASR engines 216 and/or a combination of the two. The ASRengines 208 may each generate one or more transcriptions of the audioinput to be sent to the NLU module 210. While only three ASR engines 208are shown, there may be any number of ASR engines 208, such as two,four, five, etc. The NLU module 210 may determine a combination of oneor more intents and one or more slots associated with each of thetranscriptions. The NLU module 210 may send the combinations of intentsand slots to the meta engine 502. The meta engine 502 may select one ormore of the combinations of intents and slots to be associated with theaudio input. In particular embodiments, the meta engine 502 may send theselected combinations to the reasoning module 214. The reasoning module214 may generate a response to the audio input based on the selectedcombinations. In particular embodiments, if there are multiple selectedcombinations, then the reasoning module 214 may resolve all of theselected combinations of intents and slots. The reasoning module 214 mayrank each result and select a result based on the ranking. As an exampleand not by way of limitation, one selected combination may comprise anintent [IN:play_music] and a [SL:song_1] and another selectedcombination may comprise an intent [IN:query] and a slot [SL:song_1].The reasoning module 214 may determine that the client system 130 waspreviously playing music, and as such, rank the selected combinationwith the intent [IN:play_music] to be higher than the other combination.The reasoning module 214 may determine that the result is to perform anaction, instruct the client system 130 to perform an action, or sendresults to the client system 130 to present to the user. In particularembodiments, the reasoning module 214 may determine that a notificationto the user indicative of an action performed needs to be sent and sendinstructions to the client system 130 to present the notification to theuser.

FIG. 6 illustrates an example diagram flow 600 of using a plurality ofselection strategies to select a combination of intents and slots togenerate a response. In particular embodiments, the meta engine 502 maycomprise a meta turn 602 to interface ASR engines 208 a-208 b, a metaruntime client 604, a plurality of ASR strategies 606, and a meta turn608 to perform a selection of a combination of intents and slots. Inparticular embodiments, the meta turn 602 may receive an audio inputfrom a client system 130. As an example and not by way of limitation,the meta turn 602 may receive the audio input from the assistantorchestrator 206. In particular embodiments, the diagram flow 600 mayoccur on the client system 130 or on a remote computing system. Afterthe meta turn 602 receives the audio input, the meta turn 602 may sendthe audio input to all of the available ASR engines 208 to process theaudio input and generate a plurality of transcriptions. The ASR engines208 may return the transcriptions to the meta runtime client 604. Whileonly two ASR engines are shown, there may be any number of ASR engines208. In particular embodiments, the meta runtime client 604 maydetermine the intents and slots associated with each transcription. Themeta runtime client 604 may receive the transcriptions from the ASRengines 208 and send the transcriptions to an NLU module 210 todetermine intents and slots associated with each transcription. The NLUmodule 210 may return the determined combinations of intents and slotsassociated with each transcription to the meta runtime client 604. Inparticular embodiments, the meta runtime client 604 may determine an ASRstrategy 606 to select a combination of intents and slots. In particularembodiments, the meta runtime client 604 may have an order of whichstrategy to initially try or start. In particular embodiments, the metaruntime client 604 may initially perform a mapping of the domainsassociated with the combinations to the ASR engines 208 through ASRstrategy 1 606 a. To do so, the ASR strategy 1 606 a may use theontology 440 to map the domains of the combinations of intents and slotsto the domains of the ASR engines. In particular embodiments, the metaruntime client 604 may use the combination associated with the domainthat maps to an ASR engine 208. As an example and not by way oflimitation, if the ASR engine 208 is associated with a music domain anda combination of intent and slots are identified as associated with themusic domain, then the meta runtime client 604 may select thatcombination of intents and slots. In particular embodiments, if thedomain of the combinations of intents and slots do not map to the ASRengine 208, then the meta runtime client 604 may use another ASRstrategy 606. The ASR strategies 606 may comprise a plurality ofselection strategies as described herein. In particular embodiments, oneASR strategy 606 may comprise a conflict or error resolution. As anexample and not by way of limitation, in the instance there is aconflict or an error, the combination of intents and slots generatedfrom the transcription of the default ASR engine 208 may be selected tobe associated with the audio input. While only three ASR strategies 606are shown, there may be any number of ASR strategies 606. After acombination of intents and slots is selected through one of the ASRstrategies 606, then the combination may be sent to the meta turn 608.The meta turn 608 may send the selected combination to a reasoningmodule 214 or another component of the assistant system 140. The outputof the meta turn 608 may be processed to determine a response to theaudio input.

FIG. 7 illustrates an example mapping 700 of automatic speechrecognition engines 208 to a domain 702 and its corresponding agents 704and available tasks 706. While the mapping 700 shows a certain number ofASR engines 208, domains 702, agents 704, and tasks 706, there may beany number of each in combination with one another. In particularembodiments, the mapping comprises an ASR engine 1 208 a and an ASRengine 2 208 b mapped to domain 1 702 a and domain 2 702 b,respectively. In particular embodiments, each domain 702 may have itsown corresponding agents 704 that are specific to that domain 702. Inparticular embodiments, each of the agents 704 may complete tasks 706that are specific to that domain 702. In particular embodiments, one ormore agents 704 may share the same task 706. As an example and not byway of limitation, agent 2 704 b may perform task 1 706 d which is thesame as task 1 706 f of agent 3 704 c. In particular embodiments, theremay be one ASR engine 208 that may have overlapping domains 702 withother ASR engines 208. As an example and not by way of limitation, ageneral ASR engine 208 may share domains 702 with other ASR engines 208.In particular embodiments, two discrete ASR engines 208 may be combinedto generate a combined ASR engine 208. In particular embodiments, eachdomain 702 may have its own set of agents 704 and tasks 706.

FIG. 8 illustrates an example process 800 of generating transcriptionsfor an audio input using a plurality of automatic speech recognitionengines. In particular embodiments, a user 802 may say “Find bestpitcher son” or something similar to that audio input 804 (as theassistant system 140 may not be able to initially ascertain what theuser intended to say). The audio input 804 may be captured by a clientsystem 130 and sent to all available ASR engines 208. As an example andnot by way of limitation, the client system 130 may send the audio input804 to all ASR engines 208 that are stored locally and/or on a remotecomputing system. Each ASR engine 208 may generate a plurality oftranscriptions 806. As an example and not by way of limitation, an ASRengine 208 a for general functions may generate transcriptions 806comprising “Find best pitchers on” 806 a, “Find best picture son” 806 b,and “Find best pitcher son” 806 c and an ASR engine 208 b formusic-related functions may generate transcriptions 806 comprising “Findbest picture song” 806 d and “Find best pitcher song” 806 e. Inparticular embodiments, the transcriptions 806 may be sent to an NLUmodule 210 to generate a plurality of combinations 808 of intents andslots. There may be a plurality of combinations 808 for eachtranscription 806. In particular embodiments, the assistant system 140may use an ASR strategy to select the combination of intents and slotsto be associated with the audio input 804. As an example and not by wayof limitation, the assistant system 140 may rank each of thecombinations of intents and slots and select the combination based onthe ranking. For instance, the assistant system 140 may determine thatthe user is not interested in sports and may eliminate transcriptions 1806 c from the list of combinations to select. As another example andnot by way of limitation, the assistant system 140 may identify theamount for each slot and intent to select a combination 808. Forinstance, the number of times “best pitcher” is mentioned exceeds thenumber of times “best picture” is mentioned and therefore is more likelyto be the entity in the slot. Additionally, the number of transcriptions806 that comprise “best pitcher” is greater than the number oftranscriptions 806 that comprise “best picture”. The assistant system140 may also determine that the number of intents for a query is greaterthan the number of intents for playing music and as such determine theintent is more likely to be a query. In particular embodiments, theassistant system 140 may determine a combination 808 that comprises thetwo most mentioned intent and slot. As an example and not by way oflimitation, the assistant system 140 may select combination 808 c. Inparticular embodiments, combination 808 b may refer to another team thatthe user is not affiliated with, but combination 808 c may refer to ateam that the user is affiliated with (e.g., has gone to a game,performed a previous query, etc.). In particular embodiments, theassistant system 140 may generate a response based on the selectedcombination 808 and send instructions to the client system 130 topresent the response to the user.

FIG. 9 illustrates an example method 900 for generating transcriptionsfor an audio input using a plurality of automatic speech recognitionengines. The method may begin at step 910, where the assistant system140 may receive, from a client system associated with a first user, afirst audio input. At step 920, the assistant system 140 may generate aplurality of transcriptions corresponding to the first audio input basedon a plurality of automatic speech recognition (ASR) engines. Inparticular embodiments, each ASR engine may be associated with arespective domain of a plurality of domains. At step 930, the assistantsystem 140 may determine, for each transcription, a combination of oneor more intents and one or more slots to be associated with thetranscription. At step 940, the assistant system 140 may select, by ameta-speech engine, one or more combinations of intents and slots fromthe plurality of combinations to be associated with the first userinput. At step 950, the assistant system 140 may generate a response tothe first audio input based on the selected combinations. At step 960,the assistant system 140 may send, to the client system, instructionsfor presenting the response to the first audio input. Particularembodiments may repeat one or more steps of the method of FIG. 9, whereappropriate. Although this disclosure describes and illustratesparticular steps of the method of FIG. 9 as occurring in a particularorder, this disclosure contemplates any suitable steps of the method ofFIG. 9 occurring in any suitable order. Moreover, although thisdisclosure describes and illustrates an example method for generatingtranscriptions for an audio input using a plurality of automatic speechrecognition engines including the particular steps of the method of FIG.9, this disclosure contemplates any suitable method for generatingtranscriptions for an audio input using a plurality of automatic speechrecognition engines including any suitable steps, which may include all,some, or none of the steps of the method of FIG. 9, where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method of FIG. 9, this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method of FIG. 9.

Social Graphs

FIG. 10 illustrates an example social graph 1000. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 1000 in one or more data stores. In particularembodiments, the social graph 1000 may include multiple nodes—which mayinclude multiple user nodes 1002 or multiple concept nodes 1004—andmultiple edges 1006 connecting the nodes. Each node may be associatedwith a unique entity (i.e., user or concept), each of which may have aunique identifier (ID), such as a unique number or username. The examplesocial graph 1000 illustrated in FIG. 10 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, a client system 130, anassistant system 140, or a third-party system 170 may access the socialgraph 1000 and related social-graph information for suitableapplications. The nodes and edges of the social graph 1000 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 1000.

In particular embodiments, a user node 1002 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 1002 correspondingto the user, and store the user node 1002 in one or more data stores.Users and user nodes 1002 described herein may, where appropriate, referto registered users and user nodes 1002 associated with registeredusers. In addition or as an alternative, users and user nodes 1002described herein may, where appropriate, refer to users that have notregistered with the social-networking system 160. In particularembodiments, a user node 1002 may be associated with informationprovided by a user or information gathered by various systems, includingthe social-networking system 160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 1002 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1002 maycorrespond to one or more web interfaces.

In particular embodiments, a concept node 1004 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node1004 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 1004 may be associated with one or more dataobjects corresponding to information associated with concept node 1004.In particular embodiments, a concept node 1004 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 1000 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 1004. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 1002 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 1004 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 1004.

In particular embodiments, a concept node 1004 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject representing an action or activity. As an example and not by wayof limitation, a third-party web interface may include a selectable iconsuch as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 1002 corresponding to the user and a conceptnode 1004 corresponding to the third-party web interface or resource andstore edge 1006 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 1000 maybe connected to each other by one or more edges 1006. An edge 1006connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1006 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 1006 connecting the first user's user node 1002 to thesecond user's user node 1002 in the social graph 1000 and store edge1006 as social-graph information in one or more of data stores 164. Inthe example of FIG. 10, the social graph 1000 includes an edge 1006indicating a friend relation between user nodes 1002 of user “A” anduser “B” and an edge indicating a friend relation between user nodes1002 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 1006 with particular attributes connectingparticular user nodes 1002, this disclosure contemplates any suitableedges 1006 with any suitable attributes connecting user nodes 1002. Asan example and not by way of limitation, an edge 1006 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in the social graph 1000 byone or more edges 1006. The degree of separation between two objectsrepresented by two nodes, respectively, is a count of edges in ashortest path connecting the two nodes in the social graph 1000. As anexample and not by way of limitation, in the social graph 1000, the usernode 1002 of user “C” is connected to the user node 1002 of user “A” viamultiple paths including, for example, a first path directly passingthrough the user node 1002 of user “B,” a second path passing throughthe concept node 1004 of company “Alme” and the user node 1002 of user“D,” and a third path passing through the user nodes 1002 and conceptnodes 1004 representing school “School_Name,” user “G,” company“Company_Name,” and user “D.” User “C” and user “A” have a degree ofseparation of two because the shortest path connecting theircorresponding nodes (i.e., the first path) includes two edges 1006.

In particular embodiments, an edge 1006 between a user node 1002 and aconcept node 1004 may represent a particular action or activityperformed by a user associated with user node 1002 toward a conceptassociated with a concept node 1004. As an example and not by way oflimitation, as illustrated in FIG. 10, a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “read” a concept, eachof which may correspond to an edge type or subtype. A concept-profileinterface corresponding to a concept node 1004 may include, for example,a selectable “check in” icon (such as, for example, a clickable “checkin” icon) or a selectable “add to favorites” icon. Similarly, after auser clicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Song_Name”) using a particular application (a third-party online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 1006 and a “used” edge (as illustrated in FIG. 10)between user nodes 1002 corresponding to the user and concept nodes 1004corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 1006 (asillustrated in FIG. 10) between concept nodes 1004 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 1006corresponds to an action performed by an external application (thethird-party online music application) on an external audio file (thesong “Song_Name”). Although this disclosure describes particular edges1006 with particular attributes connecting user nodes 1002 and conceptnodes 1004, this disclosure contemplates any suitable edges 1006 withany suitable attributes connecting user nodes 1002 and concept nodes1004. Moreover, although this disclosure describes edges between a usernode 1002 and a concept node 1004 representing a single relationship,this disclosure contemplates edges between a user node 1002 and aconcept node 1004 representing one or more relationships. As an exampleand not by way of limitation, an edge 1006 may represent both that auser likes and has used at a particular concept. Alternatively, anotheredge 1006 may represent each type of relationship (or multiples of asingle relationship) between a user node 1002 and a concept node 1004(as illustrated in FIG. 10 between user node 1002 for user “E” andconcept node 1004 for “online music application”).

In particular embodiments, the social-networking system 160 may createan edge 1006 between a user node 1002 and a concept node 1004 in thesocial graph 1000. As an example and not by way of limitation, a userviewing a concept-profile interface (such as, for example, by using aweb browser or a special-purpose application hosted by the user's clientsystem 130) may indicate that he or she likes the concept represented bythe concept node 1004 by clicking or selecting a “Like” icon, which maycause the user's client system 130 to send to the social-networkingsystem 160 a message indicating the user's liking of the conceptassociated with the concept-profile interface. In response to themessage, the social-networking system 160 may create an edge 1006between user node 1002 associated with the user and concept node 1004,as illustrated by “like” edge 1006 between the user and concept node1004. In particular embodiments, the social-networking system 160 maystore an edge 1006 in one or more data stores. In particularembodiments, an edge 1006 may be automatically formed by thesocial-networking system 160 in response to a particular user action. Asan example and not by way of limitation, if a first user uploads apicture, reads a book, watches a movie, or listens to a song, an edge1006 may be formed between user node 1002 corresponding to the firstuser and concept nodes 1004 corresponding to those concepts. Althoughthis disclosure describes forming particular edges 1006 in particularmanners, this disclosure contemplates forming any suitable edges 1006 inany suitable manner.

Vector Spaces and Embeddings

FIG. 11 illustrates an example view of a vector space 1100. Inparticular embodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 1100 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 1100 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 1100 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 1100(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 1110, 1120, and 1130 may be represented as pointsin the vector space 1100, as illustrated in FIG. 11. An n-gram may bemapped to a respective vector representation. As an example and not byway of limitation, n-grams t₁ and t₂ may be mapped to vectors {rightarrow over (v₁)} {right arrow over (v₂)} and in the vector space 1100,respectively, by applying a function defined by a dictionary, such that{right arrow over (v₁)}={right arrow over (π)}(t₁) and {right arrow over(v₂)}={right arrow over (π)}(t₂). As another example and not by way oflimitation, a dictionary trained to map text to a vector representationmay be utilized, or such a dictionary may be itself generated viatraining. As another example and not by way of limitation, aword-embeddings model may be used to map an n-gram to a vectorrepresentation in the vector space 1100. In particular embodiments, ann-gram may be mapped to a vector representation in the vector space 1100by using a machine leaning model (e.g., a neural network). The machinelearning model may have been trained using a sequence of training data(e.g., a corpus of objects each comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 1100 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors {right arrow over (v₁)} and {right arrow over(v₂)} in the vector space 1100, respectively, by applying a function{right arrow over (π)}, such that {right arrow over (v₁)}={right arrowover (π)}(e₁) and {right arrow over (v₂)}={right arrow over (π)}(e₂). Inparticular embodiments, an object may be mapped to a vector based on oneor more properties, attributes, or features of the object, relationshipsof the object with other objects, or any other suitable informationassociated with the object. As an example and not by way of limitation,a function may map objects to vectors by feature extraction, which maystart from an initial set of measured data and build derived values(e.g., features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function i may map the objectto a vector using a transformed reduced set of features (e.g., featureselection). In particular embodiments, a function {right arrow over (π)}may map an object e to a vector {right arrow over (π)}(e) based on oneor more n-grams associated with object e. Although this disclosuredescribes representing an n-gram or an object in a vector space in aparticular manner, this disclosure contemplates representing an n-gramor an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 1100. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of {right arrow over (v₁)} and {right arrow over (v₂)}may be a cosine similarity

$\frac{\overset{\rightharpoonup}{v_{1}} \cdot \overset{\rightharpoonup}{v_{2}}}{{\overset{\rightharpoonup}{v_{1}}}{\overset{\rightharpoonup}{v_{2}}}}.$

As another example and not by way of limitation, a similarity metric of{right arrow over (v₁)} and {right arrow over (v₂)} may be a Euclideandistance ∥{right arrow over (v₁)}−{right arrow over (v₂)}∥. A similaritymetric of two vectors may represent how similar the two objects orn-grams corresponding to the two vectors, respectively, are to oneanother, as measured by the distance between the two vectors in thevector space 1100. As an example and not by way of limitation, vector1110 and vector 1120 may correspond to objects that are more similar toone another than the objects corresponding to vector 1110 and vector1130, based on the distance between the respective vectors. Althoughthis disclosure describes calculating a similarity metric betweenvectors in a particular manner, this disclosure contemplates calculatinga similarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 12 illustrates an example artificial neural network (“ANN”) 1200.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 1200 may comprise an inputlayer 1210, hidden layers 1220, 1230, 1240, and an output layer 1250.Each layer of the ANN 1200 may comprise one or more nodes, such as anode 1205 or a node 1215. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 1210 may be connected toone of more nodes of the hidden layer 1220. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 12 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 12 depicts a connection between each node of the inputlayer 1210 and each node of the hidden layer 1220, one or more nodes ofthe input layer 1210 may not be connected to one or more nodes of thehidden layer 1220.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 1220 may comprise the output of one or morenodes of the input layer 1210. As another example and not by way oflimitation, the input to each node of the output layer 1250 may comprisethe output of one or more nodes of the hidden layer 1240. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N −1, x may be the input into residual block N−1. Although this disclosure describes a particular ANN, this disclosurecontemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k) (S_(k))=max (0, s_(k)), or any other suitablefunction F_(k)(s_(k)), where s_(k) may be the effective input to node k.In particular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection1225 between the node 1205 and the node 1215 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 1205 is used as an input to the node 1215. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k) (S_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 1200 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 1004 corresponding to a particular photo mayhave a privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 1000. A privacy setting may bespecified for one or more edges 1006 or edge-types of the social graph1000, or with respect to one or more nodes 1002, 1004 or node-types ofthe social graph 1000. The privacy settings applied to a particular edge1006 connecting two nodes may control whether the relationship betweenthe two entities corresponding to the nodes is visible to other users ofthe online social network. Similarly, the privacy settings applied to aparticular node may control whether the user or concept corresponding tothe node is visible to other users of the online social network. As anexample and not by way of limitation, a first user may share an objectto the social-networking system 160. The object may be associated with aconcept node 1004 connected to a user node 1002 of the first user by anedge 1006. The first user may specify privacy settings that apply to aparticular edge 1006 connecting to the concept node 1004 of the object,or may specify privacy settings that apply to all edges 1006 connectingto the concept node 1004. As another example and not by way oflimitation, the first user may share a set of objects of a particularobject-type (e.g., a set of images). The first user may specify privacysettings with respect to all objects associated with the first user ofthat particular object-type as having a particular privacy setting(e.g., specifying that all images posted by the first user are visibleonly to friends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such image may be used only for alimited purpose (e.g., authentication, tagging the user in photos), andfurther specify that such image may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160.

Systems and Methods

FIG. 13 illustrates an example computer system 1300. In particularembodiments, one or more computer systems 1300 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1300 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1300 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1300.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1300. This disclosure contemplates computer system 1300 taking anysuitable physical form. As example and not by way of limitation,computer system 1300 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1300 may include one or more computersystems 1300; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1300 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1300 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1300 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1300 includes a processor1302, memory 1304, storage 1306, an input/output (I/O) interface 1308, acommunication interface 1310, and a bus 1312. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1302 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1302 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1304, or storage 1306; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1304, or storage 1306. In particularembodiments, processor 1302 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1302 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1302 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1304 or storage 1306, and the instruction caches may speed upretrieval of those instructions by processor 1302. Data in the datacaches may be copies of data in memory 1304 or storage 1306 forinstructions executing at processor 1302 to operate on; the results ofprevious instructions executed at processor 1302 for access bysubsequent instructions executing at processor 1302 or for writing tomemory 1304 or storage 1306; or other suitable data. The data caches mayspeed up read or write operations by processor 1302. The TLBs may speedup virtual-address translation for processor 1302. In particularembodiments, processor 1302 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1302 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1302 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1302. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1304 includes main memory for storinginstructions for processor 1302 to execute or data for processor 1302 tooperate on. As an example and not by way of limitation, computer system1300 may load instructions from storage 1306 or another source (such as,for example, another computer system 1300) to memory 1304. Processor1302 may then load the instructions from memory 1304 to an internalregister or internal cache. To execute the instructions, processor 1302may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1302 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1302 may then write one or more of those results to memory 1304. Inparticular embodiments, processor 1302 executes only instructions in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1302 to memory 1304. Bus 1312 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1302 and memory 1304and facilitate accesses to memory 1304 requested by processor 1302. Inparticular embodiments, memory 1304 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1304 may include one ormore memories 1304, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1306 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1306 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1306 may include removable or non-removable (or fixed)media, where appropriate. Storage 1306 may be internal or external tocomputer system 1300, where appropriate. In particular embodiments,storage 1306 is non-volatile, solid-state memory. In particularembodiments, storage 1306 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1306taking any suitable physical form. Storage 1306 may include one or morestorage control units facilitating communication between processor 1302and storage 1306, where appropriate. Where appropriate, storage 1306 mayinclude one or more storages 1306. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1308 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1300 and one or more I/O devices. Computersystem 1300 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1300. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1308 for them. Where appropriate, I/Ointerface 1308 may include one or more device or software driversenabling processor 1302 to drive one or more of these I/O devices. I/Ointerface 1308 may include one or more I/O interfaces 1308, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1310 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1300 and one or more other computer systems 1300 or oneor more networks. As an example and not by way of limitation,communication interface 1310 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1310 for it. As an example and not by way oflimitation, computer system 1300 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1300 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1300 may include any suitable communicationinterface 1310 for any of these networks, where appropriate.Communication interface 1310 may include one or more communicationinterfaces 1310, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1312 includes hardware, software, or bothcoupling components of computer system 1300 to each other. As an exampleand not by way of limitation, bus 1312 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1312may include one or more buses 1312, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by a client system:receiving, at the client system via a client-side assistant process, afirst audio input from a first user; generating, by the client system, aplurality of transcriptions corresponding to the first audio input basedon a plurality of client-side automatic speech recognition (ASR)engines, wherein each ASR engine is associated with a respective domainof a plurality of domains; determining, by the client system, for eachtranscription, a combination of one or more tasks and one or moreentities to be associated with the transcription; selecting, by theclient system, one or more combinations of tasks and entities from theplurality of combinations to be associated with the first audio inputbased on a determined selection strategy; and presenting, at the clientsystem via the client-side assistant process, a response to the firstaudio input based on the selected combinations.
 2. The method of claim1, wherein each ASR engine is associated with one or more agents of aplurality of agents specific to the respective ASR engine.
 3. The methodof claim 1, wherein each domain of the plurality of domains comprisesone or more agents specific to the respective domain.
 4. The method ofclaim 3, wherein the one or more agents comprise one or more of afirst-party agent or a third-party agent.
 5. The method of claim 1,wherein each domain of the plurality of domains comprises a set of tasksspecific to the respective domain.
 6. The method of claim 1, wherein theplurality of domains are associated with a plurality of agents, andwherein each agent is operable to execute one or more tasks specific toone or more of the domains.
 7. The method of claim 1, furthercomprising: identifying, for each combination of tasks and entities, adomain of the plurality of domains, wherein selecting the one or morecombinations of tasks and entities comprises mapping the domain of eachcombination of tasks and entities to the domain associated with one ofthe plurality of ASR engines.
 8. The method of claim 7, wherein the oneor more combination of tasks and entities are selected when the domainof the respective combination of tasks and entities matches the domainof one of the plurality of ASR engines.
 9. The method of claim 1,wherein generating the plurality of transcriptions comprises: sendingthe first audio input to each of the ASR engines of the plurality of ASRengines; and receiving the plurality of transcriptions from theplurality of ASR engines.
 10. The method of claim 1, wherein one or moreof the ASR engines of the plurality of ASR engines are third-party ASRengines associated with third-party systems that are separate from andexternal to the one or more computing systems, the method furthercomprising: sending, to one of the third-party ASR engines, the firstaudio input to generate one or more transcriptions; and receiving, fromthe one of the third-party ASR engines, the one or more transcriptionsgenerated by the third-party ASR engine, wherein generating theplurality of transcriptions comprises selecting the one or moretranscriptions generated from the third-party ASR engine to determinethe combination of tasks and entities associated with each respectivetranscription.
 11. The method of claim 1, further comprising:identifying one or more features for each combination of tasks andentities, wherein the one or more features are indicative of whether thecombination of tasks and entities have an attribute; and ranking theplurality of combinations based on their respective identified features,wherein selecting the one or more combinations of tasks and entitiescomprises selecting the one or more combinations of intents and slotsbased on the ranking of the plurality of combinations.
 12. The method ofclaim 1, further comprising: identifying one or more same combinationsof intents and slots from the plurality of combinations; and ranking theone or more same combinations of intents and slots based on the numberof same combinations of intents and slots, wherein selecting the one ormore combinations of intents and slots comprises using the ranking ofthe one or more same combinations of intents and slots.
 13. The methodof claim 1, further comprising: sending the selected combinations to aplurality of agents; receiving a plurality of responses from theplurality of agents corresponding to the selected combinations; rankingthe plurality of responses received from the plurality of agents;selecting the response from the plurality of responses based on theranking of the plurality of responses; and generating the response tothe first audio input based on the selected response.
 14. The method ofclaim 1, wherein one of the plurality of ASR engines is a combined ASRengine based on two or more discrete ASR engines, and wherein each ofthe two or more discrete ASR engines is associated with a separatedomain of the plurality of domains.
 15. The method of claim 1, whereinthe response comprises one or more of an action to be performed or oneor more results generated from a query.
 16. The method of claim 1,wherein presenting the response comprises presenting a notification ofthe action to be performed or a list of one or more results.
 17. Themethod of claim 1, further comprising: determining a selection strategyout of a plurality of selection strategies to select the one or morecombination of tasks and entities based on a predetermined order ofselection strategies.
 18. The method of claim 17, wherein a firstselection strategy of the predetermined order of selection strategiesuses an ontology to map the one or more combinations of tasks andentities to a respective ASR engine, and wherein a second selectionstrategy of the predetermined order of selection strategies is used inresponse to the first selection strategy failing to map the one or morecombinations of tasks and entities to the respective ASR engine.
 19. Oneor more computer-readable non-transitory storage media embodyingsoftware that is operable when executed to: receive, at a client systemvia a client-side assistant process, a first audio input from a firstuser; generate, by the client system, a plurality of transcriptionscorresponding to the first audio input based on a plurality ofclient-side automatic speech recognition (ASR) engines, wherein each ASRengine is associated with a respective domain of a plurality of domains;determine, by the client system, for each transcription, a combinationof one or more tasks and one or more entities to be associated with thetranscription; select, by the client system, one or more combinations oftasks and entities from the plurality of combinations to be associatedwith the first audio input based on a determined selection strategy; andpresent, at the client system via the client-side assistant process, aresponse to the first audio input based on the selected combinations.20. A system comprising: one or more processors; and a non-transitorymemory coupled to the processors comprising instructions executable bythe processors, the processors operable when executing the instructionsto: receive, at a client system via a client-side assistant process, afirst audio input from a first user; generate, by the client system, aplurality of transcriptions corresponding to the first audio input basedon a plurality of client-side automatic speech recognition (ASR)engines, wherein each ASR engine is associated with a respective domainof a plurality of domains; determine, by the client system, for eachtranscription, a combination of one or more tasks and one or moreentities to be associated with the transcription; select, by the clientsystem, one or more combinations of tasks and entities from theplurality of combinations to be associated with the first audio inputbased on a determined selection strategy; and present, at the clientsystem via the client-side assistant process, a response to the firstaudio input based on the selected combinations.